# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2020, The TensorCircuit Authors
# This file is distributed under the same license as the tensorcircuit
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2022.
#
msgid ""
msgstr ""
"Project-Id-Version:  tensorcircuit\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2026-06-06 15:34+0800\n"
"PO-Revision-Date: 2022-04-13 14:58+0800\n"
"Last-Translator: Xinghan Yang\n"
"Language: cn\n"
"Language-Team: cn <LL@li.org>\n"
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"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Generated-By: Babel 2.18.0\n"

#: ../../source/api/about.rst:2
msgid "tensorcircuit.about"
msgstr ""

#: of tensorcircuit.about:1 tensorcircuit.about.about:1
msgid "Prints the information for tensorcircuit installation and environment."
msgstr ""

#: of tensorcircuit.about.cite:1
msgid ""
"Returns the citation information for tensorcircuit. Please cite our work "
"if you use the package in your research."
msgstr ""

#: ../../source/api/about.rst ../../source/api/abstractcircuit.rst
#: ../../source/api/analogcircuit.rst ../../source/api/applications/dqas.rst
#: ../../source/api/applications/finance/portfolio.rst
#: ../../source/api/applications/graphdata.rst
#: ../../source/api/applications/layers.rst
#: ../../source/api/applications/optimization.rst
#: ../../source/api/applications/utils.rst
#: ../../source/api/applications/vags.rst ../../source/api/applications/van.rst
#: ../../source/api/applications/vqes.rst
#: ../../source/api/backends/backend_factory.rst
#: ../../source/api/backends/cupy_backend.rst
#: ../../source/api/backends/jax_backend.rst
#: ../../source/api/backends/numpy_backend.rst
#: ../../source/api/backends/pytorch_backend.rst
#: ../../source/api/backends/tensorflow_backend.rst
#: ../../source/api/basecircuit.rst ../../source/api/channels.rst
#: ../../source/api/circuit.rst ../../source/api/cloud/abstraction.rst
#: ../../source/api/cloud/apis.rst ../../source/api/cloud/tencent.rst
#: ../../source/api/cloud/utils.rst ../../source/api/cloud/wrapper.rst
#: ../../source/api/compiler/composed_compiler.rst
#: ../../source/api/compiler/qiskit_compiler.rst ../../source/api/cons.rst
#: ../../source/api/densitymatrix.rst ../../source/api/experimental.rst
#: ../../source/api/fgs.rst ../../source/api/gates.rst
#: ../../source/api/interfaces/jax.rst ../../source/api/interfaces/numpy.rst
#: ../../source/api/interfaces/scipy.rst
#: ../../source/api/interfaces/tensorflow.rst
#: ../../source/api/interfaces/tensortrans.rst
#: ../../source/api/interfaces/torch.rst ../../source/api/keras.rst
#: ../../source/api/mps_base.rst ../../source/api/mpscircuit.rst
#: ../../source/api/noisemodel.rst ../../source/api/pauliprop.rst
#: ../../source/api/quantum.rst ../../source/api/quditcircuit.rst
#: ../../source/api/quditgates.rst ../../source/api/results/counts.rst
#: ../../source/api/results/qem/qem_methods.rst
#: ../../source/api/results/readout_mitigation.rst ../../source/api/shadows.rst
#: ../../source/api/simplify.rst ../../source/api/stabilizercircuit.rst
#: ../../source/api/symbolcircuit.rst ../../source/api/templates/ansatz.rst
#: ../../source/api/templates/blocks.rst
#: ../../source/api/templates/conversions.rst
#: ../../source/api/templates/graphs.rst
#: ../../source/api/templates/hamiltonians.rst
#: ../../source/api/templates/lattice.rst
#: ../../source/api/templates/measurements.rst ../../source/api/timeevol.rst
#: ../../source/api/torchnn.rst ../../source/api/translation.rst
#: ../../source/api/u1circuit.rst ../../source/api/utils.rst
#: ../../source/api/vis.rst ../../source/api/zx/converter.rst
#: ../../source/api/zx/evaluator.rst ../../source/api/zx/noise_model.rst
#: ../../source/api/zx/scalar_graph.rst ../../source/api/zx/simplifier.rst
#: ../../source/api/zx/stabilizertcircuit.rst ../../source/api/zx/utils.rst
#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.evaluate
#: keras.src.layers.layer.Layer.stateless_call
#: keras.src.models.model.Model.export
#: keras.src.models.model.Model.load_weights
#: keras.src.models.model.Model.quantize keras.src.models.model.Model.save
#: keras.src.models.model.Model.save_weights of
#: tensorcircuit.experimental.dynamics_matrix tensorcircuit.experimental.qng
#: tensorcircuit.mpscircuit.MPSCircuit.wavefunction
#: tensorcircuit.results.counts.count2vec
#: tensorcircuit.results.counts.marginal_count
#: tensorcircuit.results.counts.merge_count
#: tensorcircuit.results.counts.normalized_count
#: tensorcircuit.results.counts.reverse_count
#: tensorcircuit.results.counts.sort_count
#: tensorcircuit.results.readout_mitigation.ReadoutMit.apply_correction
#: tensorcircuit.templates.conversions.QUBO_to_Ising
#: tensorcircuit.translation.qir2cirq
#: tensorcircuit.zx.converter.pauli_channel_2
#: tensorcircuit.zx.noise_model.pauli_channel_2_probs
#: torch.nn.modules.module.Module.apply torch.nn.modules.module.Module.buffers
#: torch.nn.modules.module.Module.named_buffers
#: torch.nn.modules.module.Module.named_modules
#: torch.nn.modules.module.Module.named_parameters
#: torch.nn.modules.module.Module.parameters
#: torch.nn.modules.module.Module.register_buffer
#: torch.nn.modules.module.Module.state_dict torch.nn.modules.module.Module.to
msgid "Parameters"
msgstr ""

#: of tensorcircuit.about.cite:4
msgid "format for bib, defaults to \"bib\""
msgstr ""

#: ../../source/api/about.rst ../../source/api/abstractcircuit.rst
#: ../../source/api/analogcircuit.rst ../../source/api/applications/dqas.rst
#: ../../source/api/applications/finance/portfolio.rst
#: ../../source/api/applications/graphdata.rst
#: ../../source/api/applications/layers.rst
#: ../../source/api/applications/optimization.rst
#: ../../source/api/applications/utils.rst
#: ../../source/api/applications/vags.rst ../../source/api/applications/van.rst
#: ../../source/api/applications/vqes.rst
#: ../../source/api/backends/backend_factory.rst
#: ../../source/api/backends/cupy_backend.rst
#: ../../source/api/backends/jax_backend.rst
#: ../../source/api/backends/numpy_backend.rst
#: ../../source/api/backends/pytorch_backend.rst
#: ../../source/api/backends/tensorflow_backend.rst
#: ../../source/api/basecircuit.rst ../../source/api/channels.rst
#: ../../source/api/circuit.rst ../../source/api/cloud/abstraction.rst
#: ../../source/api/cloud/apis.rst ../../source/api/cloud/tencent.rst
#: ../../source/api/cloud/utils.rst ../../source/api/cloud/wrapper.rst
#: ../../source/api/compiler/qiskit_compiler.rst ../../source/api/cons.rst
#: ../../source/api/densitymatrix.rst ../../source/api/experimental.rst
#: ../../source/api/fgs.rst ../../source/api/gates.rst
#: ../../source/api/interfaces/jax.rst ../../source/api/interfaces/numpy.rst
#: ../../source/api/interfaces/scipy.rst
#: ../../source/api/interfaces/tensorflow.rst
#: ../../source/api/interfaces/tensortrans.rst
#: ../../source/api/interfaces/torch.rst ../../source/api/keras.rst
#: ../../source/api/mps_base.rst ../../source/api/mpscircuit.rst
#: ../../source/api/noisemodel.rst ../../source/api/pauliprop.rst
#: ../../source/api/quantum.rst ../../source/api/quditcircuit.rst
#: ../../source/api/quditgates.rst ../../source/api/results/counts.rst
#: ../../source/api/results/qem/qem_methods.rst
#: ../../source/api/results/readout_mitigation.rst
#: ../../source/api/simplify.rst ../../source/api/stabilizercircuit.rst
#: ../../source/api/symbolcircuit.rst ../../source/api/templates/ansatz.rst
#: ../../source/api/templates/blocks.rst
#: ../../source/api/templates/conversions.rst
#: ../../source/api/templates/graphs.rst
#: ../../source/api/templates/hamiltonians.rst
#: ../../source/api/templates/lattice.rst
#: ../../source/api/templates/measurements.rst ../../source/api/timeevol.rst
#: ../../source/api/torchnn.rst ../../source/api/translation.rst
#: ../../source/api/u1circuit.rst ../../source/api/utils.rst
#: ../../source/api/vis.rst ../../source/api/zx/converter.rst
#: ../../source/api/zx/evaluator.rst ../../source/api/zx/noise_model.rst
#: ../../source/api/zx/scalar_graph.rst ../../source/api/zx/simplifier.rst
#: ../../source/api/zx/stabilizertcircuit.rst ../../source/api/zx/utils.rst
#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.evaluate
#: keras.src.layers.layer.Layer.stateless_call
#: keras.src.models.model.Model.get_state_tree of
#: tensorcircuit.experimental.dynamics_matrix tensorcircuit.experimental.qng
#: tensorcircuit.mpscircuit.MPSCircuit.wavefunction
#: tensorcircuit.results.counts.count2vec
#: tensorcircuit.results.counts.marginal_count
#: tensorcircuit.results.counts.merge_count
#: tensorcircuit.results.counts.normalized_count
#: tensorcircuit.results.counts.reverse_count
#: tensorcircuit.results.counts.sort_count tensorcircuit.translation.qir2cirq
#: torch.nn.modules.module.Module.apply
#: torch.nn.modules.module.Module.state_dict torch.nn.modules.module.Module.to
msgid "Returns"
msgstr ""

#: of tensorcircuit.about.cite:6
msgid "the citation information"
msgstr ""

#: ../../source/api/about.rst ../../source/api/abstractcircuit.rst
#: ../../source/api/analogcircuit.rst ../../source/api/applications/dqas.rst
#: ../../source/api/applications/graphdata.rst
#: ../../source/api/applications/van.rst ../../source/api/applications/vqes.rst
#: ../../source/api/backends/backend_factory.rst
#: ../../source/api/backends/cupy_backend.rst
#: ../../source/api/backends/jax_backend.rst
#: ../../source/api/backends/numpy_backend.rst
#: ../../source/api/backends/pytorch_backend.rst
#: ../../source/api/backends/tensorflow_backend.rst
#: ../../source/api/basecircuit.rst ../../source/api/channels.rst
#: ../../source/api/circuit.rst ../../source/api/cloud/abstraction.rst
#: ../../source/api/cloud/apis.rst ../../source/api/cloud/tencent.rst
#: ../../source/api/cloud/wrapper.rst
#: ../../source/api/compiler/qiskit_compiler.rst ../../source/api/cons.rst
#: ../../source/api/densitymatrix.rst ../../source/api/experimental.rst
#: ../../source/api/fgs.rst ../../source/api/gates.rst
#: ../../source/api/interfaces/jax.rst ../../source/api/interfaces/numpy.rst
#: ../../source/api/interfaces/scipy.rst
#: ../../source/api/interfaces/tensorflow.rst
#: ../../source/api/interfaces/tensortrans.rst
#: ../../source/api/interfaces/torch.rst ../../source/api/keras.rst
#: ../../source/api/mps_base.rst ../../source/api/mpscircuit.rst
#: ../../source/api/noisemodel.rst ../../source/api/pauliprop.rst
#: ../../source/api/quantum.rst ../../source/api/quditcircuit.rst
#: ../../source/api/quditgates.rst ../../source/api/results/counts.rst
#: ../../source/api/results/qem/qem_methods.rst
#: ../../source/api/results/readout_mitigation.rst ../../source/api/shadows.rst
#: ../../source/api/simplify.rst ../../source/api/stabilizercircuit.rst
#: ../../source/api/symbolcircuit.rst ../../source/api/templates/blocks.rst
#: ../../source/api/templates/conversions.rst
#: ../../source/api/templates/graphs.rst
#: ../../source/api/templates/hamiltonians.rst
#: ../../source/api/templates/lattice.rst
#: ../../source/api/templates/measurements.rst ../../source/api/timeevol.rst
#: ../../source/api/torchnn.rst ../../source/api/translation.rst
#: ../../source/api/u1circuit.rst ../../source/api/utils.rst
#: ../../source/api/vis.rst ../../source/api/zx/converter.rst
#: ../../source/api/zx/evaluator.rst ../../source/api/zx/noise_model.rst
#: ../../source/api/zx/scalar_graph.rst ../../source/api/zx/simplifier.rst
#: ../../source/api/zx/stabilizertcircuit.rst ../../source/api/zx/utils.rst
#: keras.src.models.model.Model.get_state_tree of
#: tensorcircuit.mpscircuit.MPSCircuit.wavefunction
#: tensorcircuit.results.counts.count2vec
#: tensorcircuit.results.counts.marginal_count
#: tensorcircuit.results.counts.merge_count
#: tensorcircuit.results.counts.normalized_count
#: tensorcircuit.results.counts.reverse_count
#: tensorcircuit.results.counts.sort_count tensorcircuit.translation.qir2cirq
#: torch.nn.modules.module.Module.apply
#: torch.nn.modules.module.Module.state_dict torch.nn.modules.module.Module.to
msgid "Return type"
msgstr ""

#: ../../source/api/abstractcircuit.rst:2
msgid "tensorcircuit.abstractcircuit"
msgstr ""

#: of tensorcircuit.abstractcircuit:1
msgid "Methods for abstract circuits independent of nodes, edges and contractions"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.append:1
msgid "append circuit ``c`` before"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.append
#: tensorcircuit.templates.measurements.any_local_measurements
#: tensorcircuit.templates.measurements.any_measurements
#: tensorcircuit.templates.measurements.heisenberg_measurements
#: tensorcircuit.templates.measurements.spin_glass_measurements
msgid "example"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.append:19
msgid "The other circuit to be appended"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.append:21
msgid ""
"the qubit indices to which ``c`` is appended on. Defaults to None, which "
"means plain concatenation."
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.append:24
#: tensorcircuit.abstractcircuit.AbstractCircuit.prepend:5
msgid "The composed circuit"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.append_from_qir:1
msgid ""
"Apply the ciurict in form of quantum intermediate representation after "
"the current cirucit."
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.append_from_qir
#: tensorcircuit.abstractcircuit.AbstractCircuit.cond_measurement
#: tensorcircuit.abstractcircuit.AbstractCircuit.draw
#: tensorcircuit.abstractcircuit.AbstractCircuit.expectation_ps
#: tensorcircuit.abstractcircuit.AbstractCircuit.from_cirq
#: tensorcircuit.abstractcircuit.AbstractCircuit.from_qir
#: tensorcircuit.abstractcircuit.AbstractCircuit.from_qiskit
#: tensorcircuit.abstractcircuit.AbstractCircuit.gate_count
#: tensorcircuit.abstractcircuit.AbstractCircuit.gate_count_by_condition
#: tensorcircuit.abstractcircuit.AbstractCircuit.to_qir
#: tensorcircuit.analogcircuit.AnalogCircuit.expectation_ps
#: tensorcircuit.backends.cupy_backend.CuPyBackend.grad
#: tensorcircuit.backends.cupy_backend.CuPyBackend.value_and_grad
#: tensorcircuit.backends.jax_backend.JaxBackend.grad
#: tensorcircuit.backends.jax_backend.JaxBackend.value_and_grad
#: tensorcircuit.backends.numpy_backend.NumpyBackend.grad
#: tensorcircuit.backends.numpy_backend.NumpyBackend.value_and_grad
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.grad
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.value_and_grad
#: tensorcircuit.backends.pytorch_backend._qr_torch
#: tensorcircuit.backends.pytorch_backend._rq_torch
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.grad
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.value_and_grad
#: tensorcircuit.backends.tensorflow_backend._qr_tf
#: tensorcircuit.backends.tensorflow_backend._rq_tf
#: tensorcircuit.basecircuit.BaseCircuit.amplitude
#: tensorcircuit.basecircuit.BaseCircuit.cond_measurement
#: tensorcircuit.basecircuit.BaseCircuit.readouterror_bs
#: tensorcircuit.basecircuit.BaseCircuit.sample_expectation_ps
#: tensorcircuit.channels.amplitudedampingchannel
#: tensorcircuit.channels.depolarizingchannel
#: tensorcircuit.channels.generaldepolarizingchannel
#: tensorcircuit.channels.isotropicdepolarizingchannel
#: tensorcircuit.channels.phasedampingchannel
#: tensorcircuit.channels.resetchannel
#: tensorcircuit.channels.thermalrelaxationchannel
#: tensorcircuit.circuit.Circuit.expectation
#: tensorcircuit.circuit.Circuit.measure_reference
#: tensorcircuit.circuit.Circuit.replace_mps_inputs
#: tensorcircuit.cloud.wrapper.batch_expectation_ps
#: tensorcircuit.cons.set_tensornetwork_backend
#: tensorcircuit.experimental.DistributedContractor:8
#: tensorcircuit.experimental.dynamics_matrix tensorcircuit.experimental.qng
#: tensorcircuit.fgs.FGSSimulator tensorcircuit.gates.bmatrix
#: tensorcircuit.gates.matrix_for_gate tensorcircuit.gates.num_to_tensor
#: tensorcircuit.interfaces.jax.jax_interface
#: tensorcircuit.interfaces.numpy.numpy_interface
#: tensorcircuit.interfaces.scipy.scipy_optimize_interface
#: tensorcircuit.interfaces.tensorflow.tensorflow_interface
#: tensorcircuit.interfaces.tensortrans.args_to_tensor
#: tensorcircuit.interfaces.torch.torch_interface
#: tensorcircuit.interfaces.torch.torch_interface_kws
#: tensorcircuit.keras.load_func tensorcircuit.keras.save_func
#: tensorcircuit.quantum.QuAdjointVector.from_tensor
#: tensorcircuit.quantum.QuOperator.from_tensor
#: tensorcircuit.quantum.QuOperator.tensor_product
#: tensorcircuit.quantum.QuScalar.from_tensor
#: tensorcircuit.quantum.QuVector.from_tensor
#: tensorcircuit.quantum.correlation_from_counts
#: tensorcircuit.quantum.count_d2s tensorcircuit.quantum.entropy
#: tensorcircuit.quantum.free_energy
#: tensorcircuit.quantum.heisenberg_hamiltonian tensorcircuit.quantum.identity
#: tensorcircuit.quantum.measurement_counts
#: tensorcircuit.quantum.quantum_constructor
#: tensorcircuit.quantum.renyi_free_energy tensorcircuit.quantum.spin_by_basis
#: tensorcircuit.quantum.trace_product tensorcircuit.results.counts.count2vec
#: tensorcircuit.results.counts.marginal_count
#: tensorcircuit.results.counts.merge_count
#: tensorcircuit.results.counts.normalized_count
#: tensorcircuit.results.counts.reverse_count
#: tensorcircuit.results.counts.sort_count
#: tensorcircuit.simplify.infer_new_shape
#: tensorcircuit.templates.lattice.get_compatible_layers
#: tensorcircuit.timeevol.hamiltonian_evol
#: tensorcircuit.torchnn.HardwareNet.__init__
#: tensorcircuit.torchnn.QuantumNet.__init__ tensorcircuit.translation.qir2cirq
#: tensorcircuit.translation.qir2qiskit tensorcircuit.translation.qiskit2tc
#: tensorcircuit.utils.append tensorcircuit.utils.return_partial
#: tensorcircuit.vis.gate_name_trans tensorcircuit.vis.qir2tex
#: tensorcircuit.vis.render_pdf tensorcircuit.zx.noise_model.ChannelSampler:18
msgid "Example"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.append_from_qir:18
msgid "The quantum intermediate representation."
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.append_from_qir:20
msgid "whether to allow channel in the qir, defaults to False"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate:1
#: tensorcircuit.basecircuit.BaseCircuit.apply_general_gate:1
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.apply_general_gate:1
msgid ""
"An implementation of this method should also append gate directionary to "
"self._qir"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.barrier_instruction:1
msgid "add a barrier instruction flag, no effect on numerical simulation"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.barrier_instruction:3
#: tensorcircuit.abstractcircuit.AbstractCircuit.measure_instruction:3
#: tensorcircuit.abstractcircuit.AbstractCircuit.reset_instruction:3
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.measure_instruction:3
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.reset_instruction:3
msgid "the corresponding qubits"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.cond_measurement:1
#: tensorcircuit.basecircuit.BaseCircuit.cond_measurement:1
msgid ""
"Measurement on z basis at ``index`` qubit based on quantum amplitude (not"
" post-selection). The highlight is that this method can return the "
"measured result as a int Tensor and thus maintained a jittable pipeline."
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.cond_measurement:16
#: tensorcircuit.basecircuit.BaseCircuit.cond_measurement:16
msgid ""
"In terms of ``DMCircuit``, this method returns nothing and the density "
"matrix after this method is kept in mixed state without knowing the "
"measuremet resuslts"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.cond_measurement:22
msgid "the qubit for the z-basis measurement"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.cond_measurement:24
msgid "0 or 1 for z measurement on up and down freedom"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.select_gate:1
msgid "Apply ``which``-th gate from ``kraus`` list, i.e. apply kraus[which]"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.select_gate:3
msgid "Tensor of shape [] and dtype int"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.select_gate:5
msgid "A list of gate in the form of ``tc.gate`` or Tensor"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.select_gate:7
msgid "the qubit lines the gate applied on"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.depolarizing2_instruction:1
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.depolarizing2_instruction:1
msgid ""
"add a 2-qubit depolarizing instruction flag, no effect on numerical "
"simulation"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.depolarizing_instruction:1
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.depolarizing_instruction:1
msgid "add a depolarizing instruction flag, no effect on numerical simulation"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.detector_instruction:1
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.detector_instruction:1
msgid "add a detector instruction flag, no effect on numerical simulation"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.detector_instruction:3
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.detector_instruction:3
msgid "the corresponding measurement record indices"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.draw:1
msgid ""
"Visualise the circuit. This method recevies the keywords as same as "
"qiskit.circuit.QuantumCircuit.draw. More details can be found here: "
"https://qiskit.org/documentation/stubs/qiskit.circuit.QuantumCircuit.draw.html."
" Interesting kws options include: ``idle_wires``(bool)"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.expectation_ps:1
#: tensorcircuit.analogcircuit.AnalogCircuit.expectation_ps:1
msgid ""
"Shortcut for Pauli string expectation. x, y, z list are for X, Y, Z "
"positions"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.expectation_ps:26
#: tensorcircuit.analogcircuit.AnalogCircuit.expectation_ps:12
#: tensorcircuit.noisemodel.sample_expectation_ps_noisfy:5
#: tensorcircuit.shadows.expectation_ps_shadow:7
msgid "sites to apply X gate, defaults to None"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.expectation_ps:28
#: tensorcircuit.analogcircuit.AnalogCircuit.expectation_ps:14
#: tensorcircuit.noisemodel.sample_expectation_ps_noisfy:7
#: tensorcircuit.shadows.expectation_ps_shadow:9
msgid "sites to apply Y gate, defaults to None"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.expectation_ps:30
#: tensorcircuit.analogcircuit.AnalogCircuit.expectation_ps:16
#: tensorcircuit.noisemodel.sample_expectation_ps_noisfy:9
#: tensorcircuit.shadows.expectation_ps_shadow:11
msgid "sites to apply Z gate, defaults to None"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.expectation_ps:32
#: tensorcircuit.analogcircuit.AnalogCircuit.expectation_ps:18
msgid ""
"or one can apply a ps structures instead of ``x``, ``y``, ``z``, e.g. [0,"
" 1, 3, 0, 2, 2] for X_1Z_2Y_4Y_5 defaults to None, ``ps`` can overwrite "
"``x``, ``y`` and ``z``"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.expectation_ps:36
#: tensorcircuit.analogcircuit.AnalogCircuit.expectation_ps:22
msgid "whether to cache and reuse the wavefunction, defaults to True"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.expectation_ps:38
#: tensorcircuit.analogcircuit.AnalogCircuit.expectation_ps:24
#: tensorcircuit.basecircuit.BaseCircuit.sample_expectation_ps:46
#: tensorcircuit.circuit.Circuit.expectation:36
#: tensorcircuit.densitymatrix.DMCircuit.expectation:8
#: tensorcircuit.noisemodel.expectation_noisfy:5
#: tensorcircuit.noisemodel.sample_expectation_ps_noisfy:11
msgid "Noise Configuration, defaults to None"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.expectation_ps:40
#: tensorcircuit.analogcircuit.AnalogCircuit.expectation:10
#: tensorcircuit.analogcircuit.AnalogCircuit.expectation_ps:26
#: tensorcircuit.basecircuit.BaseCircuit.sample_expectation_ps:48
#: tensorcircuit.circuit.Circuit.expectation:38
#: tensorcircuit.noisemodel.expectation_noisfy:7
#: tensorcircuit.quditcircuit.QuditCircuit.expectation:10
msgid ""
"repetition time for Monte Carlo sampling for noisfy calculation, defaults"
" to 1000"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.expectation_ps:42
#: tensorcircuit.analogcircuit.AnalogCircuit.expectation_ps:28
#: tensorcircuit.basecircuit.BaseCircuit.sample_expectation_ps:50
#: tensorcircuit.densitymatrix.DMCircuit.expectation:10
#: tensorcircuit.noisemodel.expectation_noisfy:9
#: tensorcircuit.noisemodel.sample_expectation_ps_noisfy:17
msgid ""
"external randomness given by tensor uniformly from [0, 1], defaults to "
"None, used for noisfy circuit sampling"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.expectation_ps:45
#: tensorcircuit.analogcircuit.AnalogCircuit.expectation_ps:31
#: tensorcircuit.stabilizercircuit.StabilizerCircuit.expectation_ps:9
#: tensorcircuit.stabilizercircuit.StabilizerCircuit.sample_expectation_ps:11
#: tensorcircuit.u1circuit.U1Circuit.expectation:7
msgid "Expectation value"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_cirq:1
msgid "Import Cirq Circuit object as a ``tc.Circuit`` object."
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_cirq:12
msgid "Cirq Circuit object"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_cirq:14
#: tensorcircuit.abstractcircuit.AbstractCircuit.from_qiskit:14
msgid "The number of qubits for the circuit"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_cirq:16
#: tensorcircuit.abstractcircuit.AbstractCircuit.from_qiskit:16
msgid "possible input wavefunction for ``tc.Circuit``, defaults to None"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_cirq:18
#: tensorcircuit.abstractcircuit.AbstractCircuit.from_qiskit:18
#: tensorcircuit.translation.qiskit2tc:19
msgid "kwargs given in Circuit.__init__ construction function, default to None."
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_cirq:20
#: tensorcircuit.abstractcircuit.AbstractCircuit.from_qiskit:24
msgid "The same circuit but as tensorcircuit object"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_json:1
msgid "load json str as a Circuit"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_json:3
#: tensorcircuit.abstractcircuit.AbstractCircuit.from_json:8
#: tensorcircuit.abstractcircuit.AbstractCircuit.from_json_file:7
#: tensorcircuit.abstractcircuit.AbstractCircuit.initial_mapping:9
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.from_dlpack:5
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.jaxy_scan:3
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.jaxy_scan:5
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.jaxy_scan:7
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.jaxy_scan:9
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.jaxy_scan:10
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.scan:4
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.scan:6
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.scan:8
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.scan:10
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.schur:3
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.to_dlpack:3
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.to_dlpack:5
#: tensorcircuit.backends.cupy_backend.CuPyBackend.arange:9
#: tensorcircuit.backends.cupy_backend.CuPyBackend.mean:9
#: tensorcircuit.backends.cupy_backend.CuPyBackend.std:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.std:12
#: tensorcircuit.backends.jax_backend.JaxBackend.arange:9
#: tensorcircuit.backends.jax_backend.JaxBackend.from_dlpack:5
#: tensorcircuit.backends.jax_backend.JaxBackend.jaxy_scan:3
#: tensorcircuit.backends.jax_backend.JaxBackend.jaxy_scan:5
#: tensorcircuit.backends.jax_backend.JaxBackend.jaxy_scan:7
#: tensorcircuit.backends.jax_backend.JaxBackend.jaxy_scan:9
#: tensorcircuit.backends.jax_backend.JaxBackend.jaxy_scan:10
#: tensorcircuit.backends.jax_backend.JaxBackend.mean:9
#: tensorcircuit.backends.jax_backend.JaxBackend.scan:4
#: tensorcircuit.backends.jax_backend.JaxBackend.scan:6
#: tensorcircuit.backends.jax_backend.JaxBackend.scan:8
#: tensorcircuit.backends.jax_backend.JaxBackend.scan:10
#: tensorcircuit.backends.jax_backend.JaxBackend.schur:3
#: tensorcircuit.backends.jax_backend.JaxBackend.std:3
#: tensorcircuit.backends.jax_backend.JaxBackend.std:12
#: tensorcircuit.backends.jax_backend.JaxBackend.to_dlpack:3
#: tensorcircuit.backends.jax_backend.JaxBackend.to_dlpack:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.arange:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.mean:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.schur:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.std:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.std:12
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.arange:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.from_dlpack:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.mean:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.std:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.std:12
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.to_dlpack:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.to_dlpack:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.arange:9
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.from_dlpack:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.mean:9
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.scan:4
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.scan:6
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.scan:8
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.scan:10
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.std:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.std:12
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.to_dlpack:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.to_dlpack:5
#: tensorcircuit.basecircuit.BaseCircuit.expectation_before:6
#: tensorcircuit.basecircuit.BaseCircuit.expectation_before:7
#: tensorcircuit.basecircuit.BaseCircuit.projected_subsystem:8
#: tensorcircuit.channels.is_hermitian_matrix:9
#: tensorcircuit.cloud.abstraction.Device.native_gates:3
#: tensorcircuit.cloud.abstraction.Task.details:8
#: tensorcircuit.cloud.abstraction.Task.get_device:3
#: tensorcircuit.cloud.abstraction.Task.state:3
#: tensorcircuit.cloud.apis.get_device:10
#: tensorcircuit.cloud.apis.get_provider:8 tensorcircuit.cloud.apis.get_task:3
#: tensorcircuit.cloud.apis.get_task:9
#: tensorcircuit.cloud.apis.get_task_details:3
#: tensorcircuit.cloud.apis.get_task_details:10
#: tensorcircuit.cloud.apis.get_token:8 tensorcircuit.cloud.apis.list_devices:7
#: tensorcircuit.cloud.apis.list_providers:3
#: tensorcircuit.cloud.apis.resubmit_task:3
#: tensorcircuit.cloud.apis.resubmit_task:7
#: tensorcircuit.cloud.apis.set_device:10
#: tensorcircuit.cloud.apis.set_provider:8
#: tensorcircuit.cloud.apis.set_token:13
#: tensorcircuit.cons.runtime_contractor:3
#: tensorcircuit.cons.set_function_contractor:3
#: tensorcircuit.experimental.jax_jitted_function_load:3
#: tensorcircuit.experimental.jax_jitted_function_save:3
#: tensorcircuit.fgs.FGSSimulator.overlap:3
#: tensorcircuit.fgs.FGSSimulator.overlap:5 tensorcircuit.gates.u_gate:16
#: tensorcircuit.interfaces.torch.torch_interface_kws:22
#: tensorcircuit.interfaces.torch.torch_interface_kws:28
#: tensorcircuit.quantum.entanglement_negativity:3
#: tensorcircuit.quantum.entanglement_negativity:5
#: tensorcircuit.quantum.entanglement_negativity:9
#: tensorcircuit.quantum.log_negativity:3
#: tensorcircuit.quantum.log_negativity:5
#: tensorcircuit.quantum.log_negativity:11
#: tensorcircuit.quantum.partial_transpose:9 tensorcircuit.quantum.ps2xyz:5
#: tensorcircuit.quantum.ps2xyz:7 tensorcircuit.quantum.reduced_wavefunction:5
#: tensorcircuit.quantum.reduced_wavefunction:11
#: tensorcircuit.quantum.u1_mask:8 tensorcircuit.quantum.xyz2ps:3
#: tensorcircuit.quantum.xyz2ps:7
#: tensorcircuit.quditcircuit.QuditCircuit.expectation_before:5
#: tensorcircuit.quditcircuit.QuditCircuit.expectation_before:6
#: tensorcircuit.results.counts.plot_histogram:6
#: tensorcircuit.results.counts.plot_histogram:8
#: tensorcircuit.templates.graphs.Grid2DCoord.lattice_graph:6
#: tensorcircuit.timeevol.evol_global:4 tensorcircuit.timeevol.evol_global:11
#: tensorcircuit.timeevol.evol_global:15 tensorcircuit.timeevol.evol_local:4
#: tensorcircuit.timeevol.evol_local:17 tensorcircuit.translation.cirq2tc:15
#: tensorcircuit.translation.eqasm2tc:3 tensorcircuit.translation.eqasm2tc:9
#: tensorcircuit.translation.qir2json:3 tensorcircuit.translation.qir2json:8
#: tensorcircuit.utils.arg_alias:3 tensorcircuit.utils.arg_alias:5
#: tensorcircuit.utils.benchmark:3 tensorcircuit.utils.benchmark:9
#: tensornetwork.backends.abstract_backend.AbstractBackend.prod:3
#: tensornetwork.backends.abstract_backend.AbstractBackend.prod:5
#: tensornetwork.backends.jax.jax_backend.JaxBackend.prod:3
#: tensornetwork.backends.jax.jax_backend.JaxBackend.prod:5
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.prod:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.prod:5
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.prod:3
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.prod:5
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.prod:3
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.prod:5
msgid "_description_"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_json:5
msgid "Extra circuit parameters in the format of ``__init__``, defaults to None"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_json_file:1
msgid "load json file and convert it to a circuit"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_json_file:3
msgid "filename"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_json_file:5
#: tensorcircuit.abstractcircuit.AbstractCircuit.initial_mapping:7
#: tensorcircuit.cloud.apis.get_provider:3 tensorcircuit.cloud.apis.get_task:5
#: tensorcircuit.cloud.apis.get_task:7
#: tensorcircuit.cloud.apis.get_task_details:5
#: tensorcircuit.cloud.apis.get_token:4 tensorcircuit.cloud.apis.get_token:6
#: tensorcircuit.cloud.apis.list_devices:3
#: tensorcircuit.cloud.apis.list_devices:5
#: tensorcircuit.cloud.apis.list_properties:3
#: tensorcircuit.cloud.apis.list_properties:5
#: tensorcircuit.cloud.apis.list_properties:7
#: tensorcircuit.cloud.apis.list_tasks:3 tensorcircuit.cloud.apis.list_tasks:5
#: tensorcircuit.cloud.apis.list_tasks:7
#: tensorcircuit.cloud.apis.resubmit_task:5
#: tensorcircuit.cloud.apis.set_provider:3 tensorcircuit.cloud.apis.set_token:5
#: tensorcircuit.cloud.apis.set_token:7 tensorcircuit.cloud.apis.submit_task:7
#: tensorcircuit.cloud.apis.submit_task:9
#: tensorcircuit.cloud.apis.submit_task:11 tensorcircuit.quantum.xyz2ps:5
#: tensorcircuit.translation.cirq2tc:11 tensorcircuit.translation.cirq2tc:13
#: tensorcircuit.translation.eqasm2tc:5 tensorcircuit.translation.stim2tc:9
#: tensorcircuit.translation.stim2tc:11
msgid "_description_, defaults to None"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_qir:1
msgid "Restore the circuit from the quantum intermediate representation."
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_qir:21
#: tensorcircuit.translation.qir2cirq:13
#: tensorcircuit.translation.qir2qiskit:14
msgid "The quantum intermediate representation of a circuit."
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_qir:23
msgid "Extra circuit parameters."
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_qir:25
msgid "The circuit have same gates in the qir."
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_qiskit:1
msgid "Import Qiskit QuantumCircuit object as a ``tc.Circuit`` object."
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_qiskit:12
msgid "Qiskit Circuit object"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.from_qiskit:20
msgid ""
"(variational) parameters for the circuit. Could be either a sequence or "
"dictionary depending on the type of parameters in the Qiskit circuit. For"
" ``ParameterVectorElement`` use sequence. For ``Parameter`` use "
"dictionary"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.gate_count:1
msgid "count the gate number of the circuit"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.gate_count:14
msgid ""
"gate name or gate name list to be counted, defaults to None (counting all"
" gates)"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.gate_count:16
msgid "the total number of all gates or gates in the ``gate_list``"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.gate_count_by_condition:1
msgid "count the number of gates that satisfy certain condition"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.gate_count_by_condition:14
msgid "the condition for counting the gate"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.gate_count_by_condition:16
msgid "the total number of all gates which satisfy the ``condition``"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.gate_summary:1
msgid "return the summary dictionary on gate type - gate count pair"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.gate_summary:3
msgid "the gate count dict by gate type"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.get_positional_logical_mapping:1
msgid ""
"Get positional logical mapping dict based on measure instruction. This "
"function is useful when we only measure part of the qubits in the "
"circuit, to process the count result from partial measurement, we must be"
" aware of the mapping, i.e. for each position in the count bitstring, "
"what is the corresponding qubits (logical) defined on the circuit"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.get_positional_logical_mapping:7
msgid "``positional_logical_mapping``"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.initial_mapping:1
msgid ""
"generate a new circuit with the qubit mapping given by "
"``logical_physical_mapping``"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.initial_mapping:3
msgid "how to map logical qubits to the physical qubits on the new circuit"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.initial_mapping:5
msgid ""
"number of qubit of the new circuit, can be different from the original "
"one, defaults to None"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.inverse:1
msgid "inverse the circuit, return a new inversed circuit"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.inverse
msgid "EXAMPLE"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.inverse:10
msgid "keywords dict for initialization the new circuit, defaults to None"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.inverse:12
msgid "the inversed circuit"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.measure_instruction:1
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.measure_instruction:1
msgid "add a measurement instruction flag, no effect on numerical simulation"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.mr_instruction:1
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.mr_instruction:1
msgid "add a measure-reset instruction flag, no effect on numerical simulation"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.mr_instruction:3
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.mr_instruction:3
msgid "the corresponding qubit"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.pauli2_instruction:1
msgid "add a 2-qubit pauli instruction flag, no effect on numerical simulation"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.pauli_instruction:1
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.pauli_instruction:1
msgid "add a pauli instruction flag, no effect on numerical simulation"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.prepend:1
msgid "prepend circuit ``c`` before"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.prepend:3
msgid "The other circuit to be prepended"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.reset_instruction:1
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.reset_instruction:1
msgid "add a reset instruction flag, no effect on numerical simulation"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.sample_detector:1
msgid "placeholder for sample detector results"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.standardize_gate:1
msgid "standardize the gate name to tc common gate sets"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.standardize_gate:3
msgid "non-standard gate name"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.standardize_gate:5
msgid "the standard gate name"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.vis_tex:1
msgid "Generate latex string based on quantikz latex package"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.vis_tex:3
msgid "Latex string that can be directly compiled via, e.g. latexit"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.to_cirq:1
msgid "Translate ``tc.Circuit`` to a cirq circuit object."
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.to_cirq:3
#: tensorcircuit.abstractcircuit.AbstractCircuit.to_qiskit:3
msgid "whether also export measurement and reset instructions"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.to_cirq:5
msgid "A cirq circuit of this circuit."
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.to_json:1
msgid "circuit dumps to json"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.to_json:3
msgid "file str to dump the json to, defaults to None, return the json str"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.to_json:5
#: tensorcircuit.translation.qir2json:5
msgid ""
"If False, keep all info for each gate, defaults to be False. If True, "
"suitable for IO since less information is required"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.to_json:8
msgid "None if dumps to file otherwise the json str"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.to_openqasm:1
msgid ""
"transform circuit to openqasm via qiskit circuit, see "
"https://qiskit.org/documentation/stubs/qiskit.circuit.QuantumCircuit.qasm.html"
" for usage on possible options for ``kws``"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.to_openqasm:5
msgid "circuit representation in openqasm format"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.to_openqasm_file:1
msgid "save the circuit to openqasm file"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.to_openqasm_file:3
msgid "the file path to save the circuit"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.to_qir:1
msgid "Return the quantum intermediate representation of the circuit."
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.to_qir:32
msgid "The quantum intermediate representation of the circuit."
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.to_qiskit:1
msgid "Translate ``tc.Circuit`` to a qiskit QuantumCircuit object."
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.to_qiskit:5
msgid "whether also export the inputs"
msgstr ""

#: of tensorcircuit.abstractcircuit.AbstractCircuit.to_qiskit:7
msgid "A qiskit object of this circuit."
msgstr ""

#: ../../source/api/analogcircuit.rst:2
msgid "tensorcircuit.analogcircuit"
msgstr ""

#: of tensorcircuit.analogcircuit:1
msgid "Analog-Digital Hybrid Circuit class wrapper only support jax backend"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogBlock:1
msgid "A data structure to hold information about an analog evolution block."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit:1
msgid ""
"A class for hybrid digital-analog quantum simulation with time-dependent "
"Hamiltonians."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__:1
msgid "Initializes the hybrid circuit."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__
msgid "param nqubits"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__:3
#: tensorcircuit.circuit.Circuit.__init__:4
#: tensorcircuit.mpscircuit.MPSCircuit.__init__:4
msgid "The number of qubits in the circuit."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__
msgid "type nqubits"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__:4
#: tensorcircuit.shadows.entropy_shadow:10
#: tensorcircuit.shadows.expectation_ps_shadow:18
msgid "int"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__
msgid "param dim"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__:5
#: tensorcircuit.circuit.Circuit.__init__:6
#: tensorcircuit.mpscircuit.MPSCircuit.__init__:6
msgid ""
"The local Hilbert space dimension per site. Qudit is supported for 2 <= d"
" <= 36."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__
msgid "type dim"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__:6
msgid ""
"If None, the dimension of the circuit will be `2`, which is a qubit "
"system."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__
msgid "param inputs"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__:7
msgid ""
"If not None, the initial state of the circuit is taken as ``inputs`` "
"instead of :math:`"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__:9
msgid "ert 0 angle^n` qubits, defaults to None."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__
msgid "type inputs"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__:11
msgid "Optional[Tensor], optional"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__
msgid "param mps_inputs"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__:12
#: tensorcircuit.circuit.Circuit.__init__:11
msgid "QuVector for a MPS like initial wavefunction."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__
msgid "type mps_inputs"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__:13
msgid "Optional[QuOperator]"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__
msgid "param split"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__:14
#: tensorcircuit.circuit.Circuit.__init__:16
#: tensorcircuit.densitymatrix.DMCircuit.__init__:19
msgid ""
"dict if two qubit gate is ready for split, including parameters for at "
"least one of ``max_singular_values`` and ``max_truncation_err``."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__
msgid "type split"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.__init__:16
msgid "Optional[Dict[str, Any]]"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.add_analog_block:1
msgid "Adds a time-dependent analog evolution block to the circuit."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.add_analog_block:3
msgid ""
"This finalizes the current digital block and prepares a new one for "
"subsequent gates."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.add_analog_block:5
msgid ""
"A function H(t) that takes a time `t` (from 0 to `time`) and returns the "
"Hamiltonian matrix at that instant."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.add_analog_block:8
msgid "The total evolution time 'T'."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.add_analog_block:10
msgid ""
"The indices of the qubits to apply the analog evolution to. Defaults None"
" for global application."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.add_analog_block:13
msgid "Keyword arguments passed directly to `tc.timeevol.ode_evolve`"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.amplitude:1
msgid "Return the amplitude for a given bitstring `l`."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.amplitude:3
msgid ""
"For state simulators, this computes :math:`\\langle l \\vert \\psi "
"\\rangle`."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.amplitude:5
#: tensorcircuit.quditcircuit.QuditCircuit.amplitude:18
msgid "Bitstring in base-`d` using `0-9A-Z`."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.amplitude:7
#: tensorcircuit.quditcircuit.QuditCircuit.amplitude:20
msgid "Complex amplitude."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.append:1
msgid ""
"Append a circuit or another AnalogCircuit to the current hybrid circuit. "
"If an AnalogCircuit is appended, its block structure is merged into the "
"current one."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.append:4
msgid "The circuit to append."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.append:6
msgid "Optional qubit indices to map the appended circuit to."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.append:8
msgid "The updated AnalogCircuit."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.current_digital_circuit:1
msgid "Returns the last (currently active) digital circuit."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.effective_circuit:1
msgid "Returns the effective circuit after all blocks have been added."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.expectation:1
#: tensorcircuit.quditcircuit.QuditCircuit.expectation:1
msgid "Compute expectation(s) of local operators."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.expectation:3
#: tensorcircuit.quditcircuit.QuditCircuit.expectation:3
msgid "Pairs of `(operator_node, [sites])` specifying where each operator acts."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.expectation:5
#: tensorcircuit.circuit.Circuit.expectation:31
#: tensorcircuit.mpscircuit.MPSCircuit.expectation:6
#: tensorcircuit.quditcircuit.QuditCircuit.expectation:5
msgid ""
"If True, then the wavefunction tensor is cached for further expectation "
"evaluation, defaults to be true."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.expectation:8
#: tensorcircuit.circuit.Circuit.expectation:34
#: tensorcircuit.quditcircuit.QuditCircuit.expectation:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.expectation:10
msgid "whether enable light cone simplification, defaults to False"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.expectation:12
#: tensorcircuit.circuit.Circuit.expectation:44
#: tensorcircuit.densitymatrix.DMCircuit.expectation:13
#: tensorcircuit.quditcircuit.QuditCircuit.expectation:13
msgid "Tensor with one element"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.inverse:1
#, python-brace-format
msgid ""
"Inverse the hybrid circuit, including digital gate sequences and analog "
"evolutions. Analog blocks are inverted by negating the Hamiltonian (H -> "
"-H), which gives the physical inverse e^{+iHT} of the evolution e^{-iHT}."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.inverse:5
msgid "The inversed AnalogCircuit."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.measure_jit:1
#: tensorcircuit.quditcircuit.QuditCircuit.measure_jit:1
msgid ""
"Take measurement on the given site indices (computational basis). This "
"method is jittable!"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.measure_jit:4
#: tensorcircuit.basecircuit.BaseCircuit.measure_jit:4
#: tensorcircuit.quditcircuit.QuditCircuit.measure_jit:4
msgid "Measure on which site (wire) index."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.measure_jit:6
#: tensorcircuit.basecircuit.BaseCircuit.measure_jit:6
#: tensorcircuit.circuit.Circuit.measure_reference:20
#: tensorcircuit.mpscircuit.MPSCircuit.measure:5
#: tensorcircuit.quditcircuit.QuditCircuit.measure_jit:6
msgid "If true, theoretical probability is also returned."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.measure_jit:8
#: tensorcircuit.basecircuit.BaseCircuit.measure_jit:8
#: tensorcircuit.mpscircuit.MPSCircuit.measure:7
#: tensorcircuit.quditcircuit.QuditCircuit.measure_jit:8
msgid "external randomness, with shape [index], defaults to None"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.measure_jit:10
#: tensorcircuit.basecircuit.BaseCircuit.measure_jit:10
#: tensorcircuit.circuit.Circuit.measure_reference:21
#: tensorcircuit.mpscircuit.MPSCircuit.measure:9
#: tensorcircuit.quditcircuit.QuditCircuit.measure_jit:10
msgid "The sample output and probability (optional) of the quantum line."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.probability:1
msgid "Get the length-`2^n` probability vector over the computational basis."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.probability:3
#: tensorcircuit.quditcircuit.QuditCircuit.probability:3
msgid "Probability vector of shape `[dim^n]`."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.sample:1
#: tensorcircuit.quditcircuit.QuditCircuit.sample:1
msgid "Batched sampling from the circuit or final state."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.sample:3
#: tensorcircuit.quditcircuit.QuditCircuit.sample:3
msgid "Number of samples. If `None`, returns a single draw."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.sample:5
#: tensorcircuit.quditcircuit.QuditCircuit.sample:5
msgid ""
"If `True`, sample from the final state (when memory allows). Prefer "
"`True` for speed."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.sample:7
#: tensorcircuit.quditcircuit.QuditCircuit.sample:7
msgid "Optional readout error model."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.sample:9
#: tensorcircuit.quditcircuit.QuditCircuit.sample:9
msgid "Output format. See :py:meth:`tensorcircuit.quantum.measurement_results`."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.sample:11
#: tensorcircuit.basecircuit.BaseCircuit.sample:28
#: tensorcircuit.cloud.abstraction.Task.results:5
#: tensorcircuit.quantum.measurement_counts:46
#: tensorcircuit.quantum.sample2all:9
#: tensorcircuit.quditcircuit.QuditCircuit.sample:18
#: tensorcircuit.u1circuit.U1Circuit.sample:16
msgid "alias for the argument ``format``"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.sample:13
#: tensorcircuit.basecircuit.BaseCircuit.sample:30
#: tensorcircuit.quditcircuit.QuditCircuit.sample:20
msgid "random generator,  defaults to None"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.sample:15
#: tensorcircuit.quditcircuit.QuditCircuit.sample:22
msgid ""
"external randomness given by tensor uniformly from [0, 1], if set, can "
"overwrite random_generator, shape [batch] for `allow_state=True` and "
"shape [batch, nqudits] for `allow_state=False` using perfect sampling "
"implementation"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.sample:19
#: tensorcircuit.basecircuit.BaseCircuit.sample:36
#: tensorcircuit.quditcircuit.QuditCircuit.sample:26
msgid ""
"when converting to count, whether keep the full size. if false, may be "
"conflict external jit, if true, may fail for large scale system with "
"actual limited count results"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.sample:22
#: tensorcircuit.basecircuit.BaseCircuit.sample:39
#: tensorcircuit.quditcircuit.QuditCircuit.sample:29
msgid ""
"List (if batch) of tuple (binary configuration tensor and corresponding "
"probability) if the format is None, and consistent with format when given"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.set_solver_options:1
msgid "set solver options globally for this circuit object"
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.state:1
msgid "Executes the full digital-analog sequence."
msgstr ""

#: of tensorcircuit.analogcircuit.AnalogCircuit.state:3
msgid "The final state vector after the full evolution"
msgstr ""

#: ../../source/api/applications.rst:2
msgid "tensorcircuit.applications"
msgstr ""

#: ../../source/api/applications/ai.rst:2
msgid "tensorcircuit.applications.ai"
msgstr ""

#: ../../source/api/applications/ai/ensemble.rst:2
msgid "tensorcircuit.applications.ai.ensemble"
msgstr ""

#: of tensorcircuit.applications.ai.ensemble:1
msgid "Useful utilities for ensemble"
msgstr ""

#: of tensorcircuit.applications.ai.ensemble.bagging.append:1
msgid "Add model to the voting method"
msgstr ""

#: of tensorcircuit.applications.ai.ensemble.bagging.eval:1
msgid ""
"Expect input data to be a 2D array which a 1D array of yTrue followed by "
"a 1D array of yPred is expected to be the components of the 2D array"
msgstr ""

#: of tensorcircuit.applications.ai.ensemble.bagging.predict:1
msgid ""
"Input data is expected to be a 2D array that the first layer is different"
" input data (into the trained models)"
msgstr ""

#: of tensorcircuit.applications.ai.ensemble.bagging.train:1
msgid ""
"Train all models in the class, **kwargs expect to receive the argus that "
"can be directly sent to tf.fit Expected to be run after finishing compile"
msgstr ""

#: ../../source/api/applications/dqas.rst:2
msgid "tensorcircuit.applications.dqas"
msgstr ""

#: of tensorcircuit.applications.dqas:1
msgid "Modules for DQAS framework"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:1
msgid "DQAS framework entrypoint"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:3
msgid ""
"function with input of data instance, circuit parameters theta and "
"structural paramter k, return tuple of objective value and gradient with "
"respect to theta"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:5
msgid "data generator as dataset"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:6
msgid "list of operations as primitive operator pool"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:7
msgid "the default layer number of the circuit ansatz"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:8
msgid ""
"shape of circuit parameter pool, in general p_stp*l, where l is the max "
"number of circuit parameters for op in the operator pool"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:10
msgid "the same as p in the most times"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:11
msgid "batch size of one epoch"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:12
msgid "prethermal update times"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:13
msgid "training epochs"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:14
msgid "parallel thread number, 0 to disable multiprocessing model by default"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:15
msgid "set verbose log to print"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:16
msgid "function to output verbose information"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:17
msgid "function return intermiediate result for final history list"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:18
msgid "cutoff probability to avoid peak distribution"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:19
msgid ""
"function accepting list of objective values and return the baseline value"
" used in the next round"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:21
msgid "return noise with the same shape as circuit parameter pool"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:22
msgid "initial values for circuit parameter pool"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:23
msgid "initial values for probabilistic model parameters"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:24
msgid "optimizer for circuit parameters theta"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:25
msgid "optimizer for model parameters alpha"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:26
msgid "optimizer for circuit parameters in prethermal stage"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:27
msgid "fixed structural parameters for prethermal training"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:28
msgid "regularization function for model parameters alpha"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search:29
msgid "regularization function for circuit parameters theta"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search_pmb:1
msgid ""
"The probabilistic model based DQAS, can use extensively for DQAS case for"
" ``NMF`` probabilistic model."
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search_pmb:3
msgid "vag func, return loss and nabla lnp"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search_pmb:4
msgid "keras model"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search_pmb:5
msgid "sample func of logic with keras model input"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search_pmb:6
msgid "input data pipeline generator"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search_pmb:7
msgid "operation pool"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search_pmb:8
msgid "depth for DQAS"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search_pmb:12
msgid "parallel kernels"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search_pmb:23
msgid "final loss function in terms of average of sub loss for each circuit"
msgstr ""

#: of tensorcircuit.applications.dqas.DQAS_search_pmb:24
msgid "derivative function for ``loss_func``"
msgstr ""

#: of tensorcircuit.applications.dqas.get_var:1
msgid ""
"Call in customized functions and grab variables within DQAS framework "
"function by var name str."
msgstr ""

#: of tensorcircuit.applications.dqas.get_var:3
msgid "The DQAS framework function"
msgstr ""

#: of tensorcircuit.applications.dqas.get_var:5
msgid "Variables within the DQAS framework"
msgstr ""

#: of tensorcircuit.applications.dqas.get_weights:1
msgid ""
"This function works only when nnp has the same shape as stp, i.e. one "
"parameter for each op."
msgstr ""

#: of tensorcircuit.applications.dqas.parallel_kernel:1
msgid "The kernel for multiprocess to run parallel in DQAS function/"
msgstr ""

#: of tensorcircuit.applications.dqas.parallel_qaoa_train:1
msgid ""
"parallel variational parameter training and search to avoid local minimum"
" not limited to qaoa setup as the function name indicates, as long as you"
" provided suitable `vag_func`"
msgstr ""

#: of tensorcircuit.applications.dqas.parallel_qaoa_train:6
msgid "data input generator for vag_func"
msgstr ""

#: of tensorcircuit.applications.dqas.parallel_qaoa_train:7
msgid "vag_kernel"
msgstr ""

#: of tensorcircuit.applications.dqas.parallel_qaoa_train:10
msgid "number of tries"
msgstr ""

#: of tensorcircuit.applications.dqas.parallel_qaoa_train:11
msgid "for optimization problem the input is in general fixed so batch is often 1"
msgstr ""

#: of tensorcircuit.applications.dqas.parallel_qaoa_train:12
msgid "number of parallel jobs"
msgstr ""

#: of tensorcircuit.applications.dqas.parallel_qaoa_train:13
msgid "mean value of normal distribution for nnp"
msgstr ""

#: of tensorcircuit.applications.dqas.parallel_qaoa_train:14
msgid "std deviation of normal distribution for nnp"
msgstr ""

#: of tensorcircuit.applications.dqas.verbose_output:1
msgid "Doesn't support prob model DQAS search."
msgstr ""

#: ../../source/api/applications/finance.rst:2
msgid "tensorcircuit.applications.finance"
msgstr ""

#: ../../source/api/applications/finance/portfolio.rst:2
msgid "tensorcircuit.applications.finance.portfolio"
msgstr ""

#: of tensorcircuit.applications.finance.portfolio:1
msgid "Supplementary functions for portfolio optimization"
msgstr ""

#: of tensorcircuit.applications.finance.portfolio.QUBO_from_portfolio:1
msgid ""
"convert portfolio parameters to a Q matrix :param cov: n-by-n covariance "
"numpy array :param mean: numpy array of means :param q: the risk "
"preference of investor :param B: budget :param t: penalty factor :return "
"Q: n-by-n symmetric Q matrix"
msgstr ""

#: of tensorcircuit.applications.finance.portfolio.StockData:1
msgid ""
"A class for converting real-world stock data to an annualized covariance "
"matrix and annualized return."
msgstr ""

#: of tensorcircuit.applications.finance.portfolio.StockData:3
msgid ""
"Attributes: - data: A list of continuous stock data in the same time "
"span. - n_stocks: The number of stocks in the data. - n_days: The number "
"of trading days in the data."
msgstr ""

#: of tensorcircuit.applications.finance.portfolio.StockData:8
msgid ""
"Methods: - __init__(self, data): Initializes the StockData object. - "
"get_return(self, decimals=5): Calculates the annualized return. - "
"get_covariance(self, decimals=5): Calculates the annualized covariance "
"matrix."
msgstr ""

#: of tensorcircuit.applications.finance.portfolio.StockData.__init__:1
msgid "Initializes the StockData object."
msgstr ""

#: of tensorcircuit.applications.finance.portfolio.StockData.__init__:3
msgid "A list of continuous stock data in the same time span."
msgstr ""

#: of tensorcircuit.applications.finance.portfolio.StockData.get_covariance:1
msgid "Calculates the annualized covariance matrix (sigma)."
msgstr ""

#: of tensorcircuit.applications.finance.portfolio.StockData.get_covariance:3
#: tensorcircuit.applications.finance.portfolio.StockData.get_return:3
msgid "Number of decimal places to round the result to (default: 5)."
msgstr ""

#: of tensorcircuit.applications.finance.portfolio.StockData.get_covariance:4
msgid ""
"The annualized covariance matrix rounded to the specified number of "
"decimals."
msgstr ""

#: of tensorcircuit.applications.finance.portfolio.StockData.get_return:1
msgid "Calculates the annualized return (mu)."
msgstr ""

#: of tensorcircuit.applications.finance.portfolio.StockData.get_return:4
msgid ""
"The annualized return as an array rounded to the specified number of "
"decimals."
msgstr ""

#: ../../source/api/applications/graphdata.rst:2
msgid "tensorcircuit.applications.graphdata"
msgstr ""

#: of tensorcircuit.applications.graphdata:1
msgid "Modules for graph instance data and more"
msgstr ""

#: of tensorcircuit.applications.graphdata.dict2graph:1
msgid "```python d = nx.to_dict_of_dicts(g) ```"
msgstr ""

#: of tensorcircuit.applications.graphdata.graph1D:1
msgid "1D PBC chain with n sites."
msgstr ""

#: of tensorcircuit.applications.graphdata.graph1D:3
msgid "The number of nodes"
msgstr ""

#: of tensorcircuit.applications.graphdata.graph1D:5
msgid "The resulted graph g"
msgstr ""

#: of tensorcircuit.applications.graphdata.reduce_edges:3
msgid "all graphs with m edge out from g"
msgstr ""

#: of tensorcircuit.applications.graphdata.reduced_ansatz:1
msgid ""
"Generate a reduced graph with given ratio of edges compared to the "
"original graph g."
msgstr ""

#: of tensorcircuit.applications.graphdata.reduced_ansatz:3
msgid "The base graph"
msgstr ""

#: of tensorcircuit.applications.graphdata.reduced_ansatz:5
msgid "number of edges kept, default half of the edges"
msgstr ""

#: of tensorcircuit.applications.graphdata.reduced_ansatz:6
msgid "The resulted reduced graph"
msgstr ""

#: of tensorcircuit.applications.graphdata.split_ansatz:1
msgid "Split the graph in exactly ``split`` piece evenly."
msgstr ""

#: of tensorcircuit.applications.graphdata.split_ansatz:3
msgid "The mother graph"
msgstr ""

#: of tensorcircuit.applications.graphdata.split_ansatz:5
msgid "The number of the graph we want to divide into, defaults to 2"
msgstr ""

#: of tensorcircuit.applications.graphdata.split_ansatz:7
msgid "List of graph instance of size ``split``"
msgstr ""

#: ../../source/api/applications/layers.rst:2
msgid "tensorcircuit.applications.layers"
msgstr ""

#: of tensorcircuit.applications.layers:1
msgid "Module for functions adding layers of circuits"
msgstr ""

#: of tensorcircuit.applications.layers.generate_cirq_gate_layer.<locals>.f:1
msgid "Hlayer"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_any_gate_layer.<locals>.f:1
msgid "anyrxlayer"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_any_gate_layer.<locals>.f:1
msgid "anyrylayer"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_any_gate_layer.<locals>.f:1
msgid "anyrzlayer"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_any_double_gate_layer.<locals>.f:1
msgid "anyswaplayer"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_any_double_gate_layer.<locals>.f:1
msgid "anyxxlayer"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_any_double_gate_layer.<locals>.f:1
msgid "anyxylayer"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_any_double_gate_layer.<locals>.f:1
msgid "anyxzlayer"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_any_double_gate_layer.<locals>.f:1
msgid "anyyxlayer"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_any_double_gate_layer.<locals>.f:1
msgid "anyyylayer"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_any_double_gate_layer.<locals>.f:1
msgid "anyyzlayer"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_any_double_gate_layer.<locals>.f:1
msgid "anyzxlayer"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_any_double_gate_layer.<locals>.f:1
msgid "anyzylayer"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_any_double_gate_layer.<locals>.f:1
msgid "anyzzlayer"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_double_gate_layer.<locals>.f:1
msgid "cnotlayer"
msgstr ""

#: of tensorcircuit.applications.layers.generate_cirq_gate_layer.<locals>.f:1
msgid "rxlayer"
msgstr ""

#: of tensorcircuit.applications.layers.generate_cirq_gate_layer.<locals>.f:1
msgid "rylayer"
msgstr ""

#: of tensorcircuit.applications.layers.generate_cirq_gate_layer.<locals>.f:1
msgid "rzlayer"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_double_gate_layer.<locals>.f:1
msgid "swaplayer"
msgstr ""

#: of tensorcircuit.applications.layers.generate_cirq_double_gate.<locals>.f:1
msgid "xxgate"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_double_gate_layer.<locals>.f:1
msgid "xxlayer"
msgstr ""

#: of tensorcircuit.applications.layers.generate_cirq_double_gate.<locals>.f:1
msgid "xygate"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_double_gate_layer.<locals>.f:1
msgid "xylayer"
msgstr ""

#: of tensorcircuit.applications.layers.generate_cirq_double_gate.<locals>.f:1
msgid "xzgate"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_double_gate_layer.<locals>.f:1
msgid "xzlayer"
msgstr ""

#: of tensorcircuit.applications.layers.generate_cirq_double_gate.<locals>.f:1
msgid "yxgate"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_double_gate_layer.<locals>.f:1
msgid "yxlayer"
msgstr ""

#: of tensorcircuit.applications.layers.generate_cirq_double_gate.<locals>.f:1
msgid "yygate"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_double_gate_layer.<locals>.f:1
msgid "yylayer"
msgstr ""

#: of tensorcircuit.applications.layers.generate_cirq_double_gate.<locals>.f:1
msgid "yzgate"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_double_gate_layer.<locals>.f:1
msgid "yzlayer"
msgstr ""

#: of tensorcircuit.applications.layers.generate_cirq_double_gate.<locals>.f:1
msgid "zxgate"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_double_gate_layer.<locals>.f:1
msgid "zxlayer"
msgstr ""

#: of tensorcircuit.applications.layers.generate_cirq_double_gate.<locals>.f:1
msgid "zygate"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_double_gate_layer.<locals>.f:1
msgid "zylayer"
msgstr ""

#: of tensorcircuit.applications.layers.generate_cirq_double_gate.<locals>.f:1
msgid "zzgate"
msgstr ""

#: of
#: tensorcircuit.applications.layers.generate_cirq_double_gate_layer.<locals>.f:1
msgid "zzlayer"
msgstr ""

#: of tensorcircuit.applications.layers.generate_any_gate_layer:1
#, python-brace-format
msgid "$$e^{-i\\theta_i \\sigma}$$"
msgstr ""

#: of tensorcircuit.applications.layers.generate_cirq_any_double_gate_layer:1
msgid ""
"The following function should be used to generate layers with special "
"case. As its soundness depends on the nature of the task or problem, it "
"doesn't always make sense."
msgstr ""

#: of tensorcircuit.applications.layers.generate_cirq_any_gate_layer:1
#: tensorcircuit.applications.layers.generate_cirq_gate_layer:1
#: tensorcircuit.applications.layers.generate_gate_layer:1
#, python-brace-format
msgid "$$e^{-i\\theta \\sigma}$$"
msgstr ""

#: ../../source/api/applications/optimization.rst:2
msgid "tensorcircuit.applications.optimization"
msgstr ""

#: of tensorcircuit.applications.optimization:1
msgid "modules for QUBO problems in QAOA"
msgstr ""

#: of tensorcircuit.applications.optimization.Ising_loss:1
msgid ""
"computes the loss function for the Ising model based on a given quantum "
"circuit, a list of Pauli terms, and corresponding weights. The offset is "
"ignored."
msgstr ""

#: of tensorcircuit.applications.optimization.Ising_loss:5
msgid "A quantum circuit object generating the state."
msgstr ""

#: of tensorcircuit.applications.optimization.Ising_loss:6
#: tensorcircuit.applications.optimization.QAOA_loss:4
#: tensorcircuit.templates.ansatz.QAOA_ansatz_for_Ising:6
msgid ""
"A list of Pauli terms, where each term is represented as a list of 0/1 "
"series."
msgstr ""

#: of tensorcircuit.applications.optimization.Ising_loss:7
#: tensorcircuit.applications.optimization.QAOA_loss:5
#: tensorcircuit.templates.ansatz.QAOA_ansatz_for_Ising:7
msgid "A list of weights corresponding to each Pauli term."
msgstr ""

#: of tensorcircuit.applications.optimization.Ising_loss
msgid "return loss"
msgstr ""

#: of tensorcircuit.applications.optimization.Ising_loss:8
msgid "A real number representing the computed loss value."
msgstr ""

#: of tensorcircuit.applications.optimization.QAOA_loss:1
msgid ""
"computes the loss function for the Quantum Approximate Optimization "
"Algorithm (QAOA) applied to the Ising model."
msgstr ""

#: of tensorcircuit.applications.optimization.QAOA_loss:3
#: tensorcircuit.templates.ansatz.QAOA_ansatz_for_Ising:5
msgid "The number of layers in the QAOA ansatz."
msgstr ""

#: of tensorcircuit.applications.optimization.QAOA_loss:6
#: tensorcircuit.templates.ansatz.QAOA_ansatz_for_Ising:4
msgid "A list of parameter values used in the QAOA ansatz."
msgstr ""

#: of tensorcircuit.applications.optimization.QAOA_loss:7
#: tensorcircuit.applications.optimization.QUBO_QAOA:15
#: tensorcircuit.templates.ansatz.QAOA_ansatz_for_Ising:8
msgid ""
"A flag indicating whether to use all-to-all coupling in mixers. Default "
"is False."
msgstr ""

#: of tensorcircuit.applications.optimization.QAOA_loss
#: tensorcircuit.applications.optimization.QUBO_QAOA
#: tensorcircuit.templates.ansatz.QAOA_ansatz_for_Ising
msgid "paran mixer (optional)"
msgstr ""

#: of tensorcircuit.applications.optimization.QAOA_loss:8
#: tensorcircuit.applications.optimization.QUBO_QAOA:12
#: tensorcircuit.templates.ansatz.QAOA_ansatz_for_Ising:9
msgid ""
"The mixer operator to use. Default is \"X\". The other options are \"XY\""
" and \"ZZ\"."
msgstr ""

#: of tensorcircuit.applications.optimization.QAOA_loss:9
msgid "The computed loss value."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA:1
msgid ""
"Performs the QAOA on a given QUBO problem. Adam optimizer from TensorFlow"
" is used."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA:4
#: tensorcircuit.applications.optimization.QUBO_QAOA_cvar:3
msgid "The n-by-n square and symmetric Q-matrix representing the QUBO problem."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA:5
#: tensorcircuit.applications.optimization.QUBO_QAOA_cvar:5
#: tensorcircuit.applications.optimization.cvar_loss:3
msgid "The number of layers (depth) in the QAOA ansatz."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA:6
msgid "The number of iterations to run the optimization."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA:7
msgid ""
"A flag indicating whether to use vectorized variational adjoint gradient."
" Default is False."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA:8
msgid ""
"The number of circuits when using vectorized variational adjoint "
"gradient. Default is 10."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA:10
msgid ""
"The initial parameters for the ansatz circuit. Default is None, which "
"initializes the parameters randomly."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA:13
msgid "The learning rate for the Adam optimizer. Default is 1e-2."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA:14
msgid ""
"A callback function that is executed during each iteration. Default is "
"None."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA
msgid "return params"
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA:16
#: tensorcircuit.applications.optimization.QUBO_QAOA_cvar:12
msgid "The optimized parameters for the ansatz circuit."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA_cvar:1
msgid "Perform the QUBO QAOA optimization with CVaR as the loss function."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA_cvar:4
msgid "The ansatz function to be used for QAOA."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA_cvar:6
#: tensorcircuit.applications.optimization.cvar_from_circuit:7
#: tensorcircuit.applications.optimization.cvar_from_expectation:5
#: tensorcircuit.applications.optimization.cvar_loss:6
msgid "The cut-off percentage for CVaR."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA_cvar:7
msgid ""
"The number of samples for measurements in the CVaR calculation. Default "
"is 1000."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA_cvar:8
msgid "A callback function to be called after each iteration. Default is None."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA_cvar:9
msgid ""
"A flag indicating the type of CVaR ansatz (measurement-based or "
"expectation-based). Default is False."
msgstr ""

#: of tensorcircuit.applications.optimization.QUBO_QAOA_cvar:11
msgid "The maximum number of iterations for the optimization. Default is 1000."
msgstr ""

#: of tensorcircuit.applications.optimization.cvar_from_circuit:1
msgid ""
"Directly calculate the Conditional Value at Risk (CVaR) from a circuit. "
"The CVaR depends on a bunch of measurements."
msgstr ""

#: of tensorcircuit.applications.optimization.cvar_from_circuit:4
msgid "The quantum circuit used to prepare the state."
msgstr ""

#: of tensorcircuit.applications.optimization.cvar_from_circuit:5
msgid "The number of samples to take for measurements."
msgstr ""

#: of tensorcircuit.applications.optimization.cvar_from_circuit:6
#: tensorcircuit.applications.optimization.cvar_from_expectation:4
#: tensorcircuit.applications.optimization.cvar_loss:4
msgid ""
"The Q-matrix representing the Quadratic Unconstrained Binary Optimization"
" (QUBO) problem."
msgstr ""

#: of tensorcircuit.applications.optimization.cvar_from_circuit:8
#: tensorcircuit.applications.optimization.cvar_from_expectation:6
#: tensorcircuit.applications.optimization.cvar_value:6
msgid "The calculated CVaR value."
msgstr ""

#: of tensorcircuit.applications.optimization.cvar_from_expectation:1
msgid ""
"Calculate the Conditional Value at Risk (CVaR) from the expectation "
"values of a quantum circuit."
msgstr ""

#: of tensorcircuit.applications.optimization.cvar_from_expectation:3
msgid "The quantum circuit."
msgstr ""

#: of tensorcircuit.applications.optimization.cvar_loss:1
msgid "Calculate the CVaR loss for a given QUBO problem using the QAOA ansatz."
msgstr ""

#: of tensorcircuit.applications.optimization.cvar_loss:5
msgid "The number of samples to take for measurements in the CVaR calculation."
msgstr ""

#: of tensorcircuit.applications.optimization.cvar_loss:7
msgid ""
"A flag indicating the type of CVaR ansatz (measurement-based or "
"expectation-based)."
msgstr ""

#: of tensorcircuit.applications.optimization.cvar_loss:8
msgid "The parameters for the QAOA ansatz circuit."
msgstr ""

#: of tensorcircuit.applications.optimization.cvar_loss:9
msgid "The calculated CVaR loss."
msgstr ""

#: of tensorcircuit.applications.optimization.cvar_value:1
msgid ""
"Compute the Conditional Value at Risk (CVaR) based on the measurement "
"results."
msgstr ""

#: of tensorcircuit.applications.optimization.cvar_value:3
msgid "The observed outcomes after measurements."
msgstr ""

#: of tensorcircuit.applications.optimization.cvar_value:4
msgid "Probabilities associated with each observed outcome."
msgstr ""

#: of tensorcircuit.applications.optimization.cvar_value:5
msgid "The cut-off percentage for CVaR computation."
msgstr ""

#: ../../source/api/applications/physics.rst:2
msgid "tensorcircuit.applications.physics"
msgstr ""

#: ../../source/api/applications/physics/baseline.rst:2
msgid "tensorcircuit.applications.physics.baseline"
msgstr ""

#: of tensorcircuit.applications.physics.baseline:1
msgid "baseline calculators for physical systems"
msgstr ""

#: ../../source/api/applications/physics/fss.rst:2
msgid "tensorcircuit.applications.physics.fss"
msgstr ""

#: of tensorcircuit.applications.physics.fss:1
msgid "finite size scaling tools"
msgstr ""

#: ../../source/api/applications/utils.rst:2
msgid "tensorcircuit.applications.utils"
msgstr ""

#: of tensorcircuit.applications.utils:1
msgid ""
"A collection of useful function snippets that irrelevant with the main "
"modules or await for further refactor"
msgstr ""

#: of tensorcircuit.applications.utils.color_svg:1
msgid "color cirq circuit SVG for given gates, a small tool to hack the cirq SVG"
msgstr ""

#: of tensorcircuit.applications.utils.color_svg:5
msgid "integer coordinate which gate is colored"
msgstr ""

#: of tensorcircuit.applications.utils.repr2array:1
msgid "transform repr form of an array to real numpy array"
msgstr ""

#: ../../source/api/applications/vags.rst:2
msgid "tensorcircuit.applications.vags"
msgstr ""

#: of tensorcircuit.applications.vags:1
msgid "DQAS application kernels as vag functions"
msgstr ""

#: of tensorcircuit.applications.vags.ave_func:1
msgid "1D array for full wavefunction, the basis is in lexcical order"
msgstr ""

#: of tensorcircuit.applications.vags.ave_func:2
#: tensorcircuit.applications.vags.energy:5
msgid "nx.Graph"
msgstr ""

#: of tensorcircuit.applications.vags.ave_func:3
msgid "transformation functions before averaged"
msgstr ""

#: of tensorcircuit.applications.vags.cvar:1
msgid "as f3"
msgstr ""

#: of tensorcircuit.applications.vags.energy:1
msgid "maxcut energy for n qubit wavefunction i-th basis"
msgstr ""

#: of tensorcircuit.applications.vags.energy:3
msgid "ranged from 0 to 2**n-1"
msgstr ""

#: of tensorcircuit.applications.vags.energy:4
#: tensorcircuit.applications.vags.unitary_design:4
#: tensorcircuit.quantum.correlation_from_samples:8
#: tensorcircuit.quantum.count_s2d:6
msgid "number of qubits"
msgstr ""

#: of tensorcircuit.applications.vags.entanglement_entropy:1
msgid ""
"deprecated as non tf and non flexible, use the combination of "
"``reduced_density_matrix`` and ``entropy`` instead."
msgstr ""

#: of tensorcircuit.applications.vags.entropy:1
#: tensorcircuit.applications.vags.reduced_density_matrix:1
msgid "deprecated, current version in tc.quantum"
msgstr ""

#: of tensorcircuit.applications.vags.evaluate_vag:1
msgid ""
"value and gradient, currently only tensorflow backend is supported jax "
"and numpy seems to be slow in circuit simulation anyhow. *deprecated*"
msgstr ""

#: of tensorcircuit.applications.vags.evaluate_vag:8
msgid "if lbd=0, take energy as objective"
msgstr ""

#: of tensorcircuit.applications.vags.evaluate_vag:9
msgid "if as default 0, overlap will not compute in the process"
msgstr ""

#: of tensorcircuit.applications.vags.gapfilling:1
msgid "Fill single qubit gates according to placeholder on circuit"
msgstr ""

#: of tensorcircuit.applications.vags.heisenberg_measurements:1
msgid "Hamiltonian measurements for Heisenberg model on graph lattice g"
msgstr ""

#: of tensorcircuit.applications.vags.q:1
msgid "short cut for ``cirq.LineQubit(i)``"
msgstr ""

#: of tensorcircuit.applications.vags.qaoa_block_vag:1
#: tensorcircuit.applications.vags.qaoa_block_vag_energy:1
msgid "QAOA block encoding kernel, support 2 params in one op"
msgstr ""

#: of tensorcircuit.applications.vags.qaoa_train:1
msgid ""
"training QAOA with only optimizing circuit parameters, can be well "
"replaced with more general function `DQAS_search`"
msgstr ""

#: of tensorcircuit.applications.vags.quantum_mp_qaoa_vag:1
msgid "multi parameter for one layer"
msgstr ""

#: of tensorcircuit.applications.vags.quantum_mp_qaoa_vag:7
#: tensorcircuit.applications.vags.quantum_qaoa_vag:7
msgid "kw arguments for measurements_func"
msgstr ""

#: of tensorcircuit.applications.vags.quantum_mp_qaoa_vag:8
msgid "loss function, gradient of nnp"
msgstr ""

#: of tensorcircuit.applications.vags.quantum_qaoa_vag:1
msgid ""
"tensorflow quantum backend compare to qaoa_vag which is tensorcircuit "
"backend"
msgstr ""

#: of tensorcircuit.applications.vags.tfim_measurements:1
msgid "Hamiltonian for tfim on lattice defined by graph g"
msgstr ""

#: of tensorcircuit.applications.vags.tfim_measurements:8
msgid "cirq.PauliSum as operators for tfq expectation layer"
msgstr ""

#: of tensorcircuit.applications.vags.unitary_design:1
msgid ""
"generate random wavefunction from approximately Haar measure, reference:"
"  https://doi.org/10.1063/1.4983266"
msgstr ""

#: of tensorcircuit.applications.vags.unitary_design:5
msgid "repetition of the blocks"
msgstr ""

#: of tensorcircuit.applications.vags.unitary_design_block:1
msgid "random Haar measure approximation"
msgstr ""

#: of tensorcircuit.applications.vags.unitary_design_block:3
msgid "cirq.Circuit, empty circuit"
msgstr ""

#: of tensorcircuit.applications.vags.unitary_design_block:4
msgid "# of qubit"
msgstr ""

#: ../../source/api/applications/van.rst:2
msgid "tensorcircuit.applications.van"
msgstr ""

#: of tensorcircuit.applications.van:1
msgid ""
"One-hot variational autoregressive models for multiple categorical "
"choices beyond binary"
msgstr ""

#: keras.src.layers.layer.Layer.add_loss:1 of
msgid "Can be called inside of the `call()` method to add a scalar loss."
msgstr ""

#: keras.src.layers.layer.Layer.add_loss:3
#: keras.src.layers.layer.Layer.stateless_call:23
#: keras.src.models.model.Model.get_state_tree:18
#: keras.src.models.model.Model.load_weights:31
#: keras.src.models.model.Model.quantize:24
#: keras.src.models.model.Model.save:25
#: keras.src.models.model.Model.save_weights:26
#: keras.src.trainers.trainer.Trainer.compile:3
#: keras.src.trainers.trainer.Trainer.compute_loss:6
#: keras.src.trainers.trainer.Trainer.compute_metrics:8 of
#: tensorcircuit.cons.OMEOptimizer:6
msgid "Example:"
msgstr ""

#: keras.src.layers.layer.Layer.add_loss:5 of
msgid "```python class MyLayer(Layer):"
msgstr ""

#: keras.src.layers.layer.Layer.add_loss:7 of
msgid "... def call(self, x):"
msgstr ""

#: keras.src.layers.layer.Layer.add_loss:9 of
msgid "self.add_loss(ops.sum(x)) return x"
msgstr ""

#: keras.src.layers.layer.Layer.add_loss:11
#: keras.src.layers.layer.Layer.stateless_call:42
#: keras.src.models.model.Model.from_config:12
#: keras.src.ops.operation.Operation.from_config:12
#: keras.src.trainers.trainer.Trainer.compute_metrics:27 of
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:21
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:35
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:21
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:35
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:19
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:33
msgid "```"
msgstr ""

#: keras.src.layers.layer.Layer.add_variable:1
#: keras.src.layers.layer.Layer.add_weight:1 of
msgid "Add a weight variable to the layer."
msgstr ""

#: keras.src.layers.layer.Layer.add_variable:3 of
msgid "Alias of `add_weight()`."
msgstr ""

#: keras.src.layers.layer.Layer.add_weight:3 of
msgid ""
"Shape tuple for the variable. Must be fully-defined (no `None` entries). "
"Defaults to `()` (scalar) if unspecified."
msgstr ""

#: keras.src.layers.layer.Layer.add_weight:5 of
msgid ""
"Initializer object to use to populate the initial variable value, or "
"string name of a built-in initializer (e.g. `\"random_normal\"`). If "
"unspecified, defaults to `\"glorot_uniform\"` for floating-point "
"variables and to `\"zeros\"` for all other types (e.g. int, bool)."
msgstr ""

#: keras.src.layers.layer.Layer.add_weight:10 of
msgid ""
"Dtype of the variable to create, e.g. `\"float32\"`. If unspecified, "
"defaults to the layer's variable dtype (which itself defaults to "
"`\"float32\"` if unspecified)."
msgstr ""

#: keras.src.layers.layer.Layer.add_weight:13 of
msgid ""
"Boolean, whether the variable should be trainable via backprop or whether"
" its updates are managed manually. Defaults to `True`."
msgstr ""

#: keras.src.layers.layer.Layer.add_weight:16 of
msgid ""
"Boolean, whether to autocast layers variables when accessing them. "
"Defaults to `True`."
msgstr ""

#: keras.src.layers.layer.Layer.add_weight:18 of
msgid ""
"Regularizer object to call to apply penalty on the weight. These "
"penalties are summed into the loss function during optimization. Defaults"
" to `None`."
msgstr ""

#: keras.src.layers.layer.Layer.add_weight:21 of
msgid ""
"Contrainst object to call on the variable after any optimizer update, or "
"string name of a built-in constraint. Defaults to `None`."
msgstr ""

#: keras.src.layers.layer.Layer.add_weight:24 of
msgid ""
"Optional string, one of `None`, `\"none\"`, `\"mean\"`, `\"sum\"` or "
"`\"only_first_replica\"`. Annotates the variable with the type of multi-"
"replica aggregation to be used for this variable when writing custom data"
" parallel training loops. Defaults to `\"none\"`."
msgstr ""

#: keras.src.layers.layer.Layer.add_weight:29 of
msgid ""
"Boolean, whether to overwrite the variable with the computed gradient. "
"This is useful for float8 training. Defaults to `False`."
msgstr ""

#: keras.src.layers.layer.Layer.add_weight:32 of
msgid "String name of the variable. Useful for debugging purposes."
msgstr ""

#: keras.src.layers.layer.Layer.build_from_config:1
#: keras.src.models.model.Model.build_from_config:1 of
msgid "Builds the layer's states with the supplied config dict."
msgstr ""

#: keras.src.layers.layer.Layer.build_from_config:3
#: keras.src.models.model.Model.build_from_config:3 of
msgid ""
"By default, this method calls the `build(config[\"input_shape\"])` "
"method, which creates weights based on the layer's input shape in the "
"supplied config. If your config contains other information needed to load"
" the layer's state, you should override this method."
msgstr ""

#: keras.src.layers.layer.Layer.build_from_config:8
#: keras.src.models.model.Model.build_from_config:8 of
msgid "Dict containing the input shape associated with this layer."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile:1 of
msgid "Configures the model for training."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile:5 of
msgid "```python model.compile("
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile:7 of
msgid ""
"optimizer=keras.optimizers.Adam(learning_rate=1e-3), "
"loss=keras.losses.BinaryCrossentropy(), metrics=["
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile:10 of
msgid "keras.metrics.BinaryAccuracy(), keras.metrics.FalseNegatives(),"
msgstr ""

#: keras.src.models.model.Model.save:32
#: keras.src.trainers.trainer.Trainer.compile:12 of
msgid "],"
msgstr ""

#: keras.src.models.model.Model.export:88
#: keras.src.models.model.Model.quantize:63
#: keras.src.trainers.trainer.Trainer.compile:14 of
#: tensorcircuit.cons.get_tn_info:25
msgid ")"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile of
msgid "param optimizer"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile:16 of
msgid "String (name of optimizer) or optimizer instance. See `keras.optimizers`."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile of
msgid "param loss"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile:18 of
msgid ""
"Loss function. May be a string (name of loss function), or a "
"`keras.losses.Loss` instance. See `keras.losses`. A loss function is any "
"callable with the signature `loss = fn(y_true, y_pred)`, where `y_true` "
"are the ground truth values, and `y_pred` are the model's predictions. "
"`y_true` should have shape `(batch_size, d0, .. dN)` (except in the case "
"of sparse loss functions such as sparse categorical crossentropy which "
"expects integer arrays of shape `(batch_size, d0, .. dN-1)`). `y_pred` "
"should have shape `(batch_size, d0, .. dN)`. The loss function should "
"return a float tensor."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile of
msgid "param loss_weights"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile:29 of
msgid ""
"Optional list or dictionary specifying scalar coefficients (Python "
"floats) to weight the loss contributions of different model outputs. The "
"loss value that will be minimized by the model will then be the *weighted"
" sum* of all individual losses, weighted by the `loss_weights` "
"coefficients.  If a list, it is expected to have a 1:1 mapping to the "
"model's outputs. If a dict, it is expected to map output names (strings) "
"to scalar coefficients."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile of
msgid "param metrics"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile:37 of
#, python-brace-format
msgid ""
"List of metrics to be evaluated by the model during training and testing."
" Each of this can be a string (name of a built-in function), function or "
"a `keras.metrics.Metric` instance. See `keras.metrics`. Typically you "
"will use `metrics=['accuracy']`. A function is any callable with the "
"signature `result = fn(y_true, _pred)`. To specify different metrics for "
"different outputs of a multi-output model, you could also pass a "
"dictionary, such as `metrics={'a':'accuracy', 'b':['accuracy', 'mse']}`. "
"You can also pass a list to specify a metric or a list of metrics for "
"each output, such as `metrics=[['accuracy'], ['accuracy', 'mse']]` or "
"`metrics=['accuracy', ['accuracy', 'mse']]`. When you pass the strings "
"'accuracy' or 'acc', we convert this to one of "
"`keras.metrics.BinaryAccuracy`, `keras.metrics.CategoricalAccuracy`, "
"`keras.metrics.SparseCategoricalAccuracy` based on the shapes of the "
"targets and of the model output. A similar conversion is done for the "
"strings `\"crossentropy\"` and `\"ce\"` as well. The metrics passed here "
"are evaluated without sample weighting; if you would like sample "
"weighting to apply, you can specify your metrics via the "
"`weighted_metrics` argument instead."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile of
msgid "param weighted_metrics"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile:60 of
msgid ""
"List of metrics to be evaluated and weighted by `sample_weight` or "
"`class_weight` during training and testing."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile of
msgid "param run_eagerly"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile:62 of
msgid ""
"Bool. If `True`, this model's forward pass will never be compiled. It is "
"recommended to leave this as `False` when training (for best "
"performance), and to set it to `True` when debugging."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile of
msgid "param steps_per_execution"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile:66 of
msgid ""
"Int. The number of batches to run during each a single compiled function "
"call. Running multiple batches inside a single compiled function call can"
" greatly improve performance on TPUs or small models with a large Python "
"overhead. At most, one full epoch will be run each execution. If a number"
" larger than the size of the epoch is passed, the execution will be "
"truncated to the size of the epoch. Note that if `steps_per_execution` is"
" set to `N`, `Callback.on_batch_begin` and `Callback.on_batch_end` "
"methods will only be called every `N` batches (i.e. before/after each "
"compiled function execution). Not supported with the PyTorch backend."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile of
msgid "param jit_compile"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile:78 of
msgid ""
"Bool or `\"auto\"`. Whether to use XLA compilation when compiling a "
"model. For `jax` and `tensorflow` backends, `jit_compile=\"auto\"` "
"enables XLA compilation if the model supports it, and disabled otherwise."
" For `torch` backend, `\"auto\"` will default to eager execution and "
"`jit_compile=True` will run with `torch.compile` with the `\"inductor\"` "
"backend."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile of
msgid "param auto_scale_loss"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile:85 of
msgid ""
"Bool. If `True` and the model dtype policy is `\"mixed_float16\"`, the "
"passed optimizer will be automatically wrapped in a `LossScaleOptimizer`,"
" which will dynamically scale the loss to prevent underflow."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile_from_config:1 of
msgid "Compiles the model with the information given in config."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile_from_config:3 of
msgid ""
"This method uses the information in the config (optimizer, loss, metrics,"
" etc.) to compile the model."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compile_from_config:6 of
msgid "Dict containing information for compiling the model."
msgstr ""

#: of tensorcircuit.applications.van.MADE.compute_dtype:1
#: tensorcircuit.applications.van.MaskedConv2D.compute_dtype:1
#: tensorcircuit.applications.van.MaskedLinear.compute_dtype:1
#: tensorcircuit.applications.van.NMF.compute_dtype:1
#: tensorcircuit.applications.van.PixelCNN.compute_dtype:1
#: tensorcircuit.applications.van.ResidualBlock.compute_dtype:1
#: tensorcircuit.applications.vqes.Linear.compute_dtype:1
#: tensorcircuit.keras.HardwareLayer.compute_dtype:1
#: tensorcircuit.keras.QuantumLayer.compute_dtype:1
msgid "The dtype of the computations performed by the layer."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:1 of
msgid "Compute the total loss, validate it, and return it."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:3 of
msgid ""
"Subclasses can optionally override this method to provide custom loss "
"computation logic."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:8 of
msgid "```python class MyModel(Model):"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:10
#: keras.src.trainers.trainer.Trainer.compute_metrics:12 of
msgid "def __init__(self, *args, **kwargs):"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:11 of
msgid ""
"super().__init__(*args, **kwargs) self.loss_tracker = "
"metrics.Mean(name='loss')"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:14 of
msgid "def compute_loss(self, x, y, y_pred, sample_weight, training=True):"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:15 of
msgid ""
"loss = ops.mean((y_pred - y) ** 2) loss += ops.sum(self.losses) "
"self.loss_tracker.update_state(loss) return loss"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:20 of
msgid "def reset_metrics(self):"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:21 of
msgid "self.loss_tracker.reset_state()"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:23 of
msgid "@property def metrics(self):"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:25 of
msgid "return [self.loss_tracker]"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:27 of
msgid ""
"inputs = layers.Input(shape=(10,), name='my_input') outputs = "
"layers.Dense(10)(inputs) model = MyModel(inputs, outputs) "
"model.add_loss(ops.sum(outputs))"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:32 of
#, python-brace-format
msgid ""
"optimizer = SGD() model.compile(optimizer, loss='mse', "
"steps_per_execution=10) dataset = ... model.fit(dataset, epochs=2, "
"steps_per_epoch=10) print(f\"Custom loss: "
"{model.loss_tracker.result()}\") ```"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:39
#: keras.src.trainers.trainer.Trainer.compute_metrics:29 of
msgid "Input data."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:40
#: keras.src.trainers.trainer.Trainer.compute_metrics:30 of
msgid "Target data."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:41 of
msgid "Predictions returned by the model (output of `model(x)`)"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:42
#: keras.src.trainers.trainer.Trainer.compute_metrics:32 of
msgid "Sample weights for weighting the loss function."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:43 of
msgid "Whether we are training or evaluating the model."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_loss:45 of
msgid ""
"The total loss as a scalar tensor, or `None` if no loss results (which is"
" the case when called by `Model.test_step`)."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_metrics:1 of
msgid "Update metric states and collect all metrics to be returned."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_metrics:3 of
msgid ""
"Subclasses can optionally override this method to provide custom metric "
"updating and collection logic. Custom metrics are not passed in "
"`compile()`, they can be created in `__init__` or `build`. They are "
"automatically tracked and returned by `self.metrics`."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_metrics:10 of
msgid "```python class MyModel(Sequential):"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_metrics:13 of
msgid ""
"super().__init__(*args, **kwargs) self.custom_metric = "
"MyMetric(name=\"custom_metric\")"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_metrics:16 of
msgid "def compute_metrics(self, x, y, y_pred, sample_weight):"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_metrics:17 of
msgid ""
"# This super call updates metrics from `compile` and returns # results "
"for all metrics listed in `self.metrics`. metric_results = "
"super().compute_metrics("
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_metrics:20 of
msgid "x, y, y_pred, sample_weight)"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_metrics:22 of
msgid ""
"# `metric_results` contains the previous result for # `custom_metric`, "
"this is where we update it. self.custom_metric.update_state(x, y, y_pred,"
" sample_weight) metric_results['custom_metric'] = "
"self.custom_metric.result() return metric_results"
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_metrics:31 of
msgid "Predictions returned by the model output of `model.call(x)`."
msgstr ""

#: keras.src.trainers.trainer.Trainer.compute_metrics:34 of
#, python-brace-format
msgid ""
"A `dict` containing values that will be passed to "
"`keras.callbacks.CallbackList.on_train_batch_end()`. Typically, the "
"values of the metrics listed in `self.metrics` are returned. Example: "
"`{'loss': 0.2, 'accuracy': 0.7}`."
msgstr ""

#: keras.src.layers.layer.Layer.count_params:1 of
msgid "Count the total number of scalars composing the weights."
msgstr ""

#: keras.src.layers.layer.Layer.count_params:3 of
msgid "An integer count."
msgstr ""

#: of tensorcircuit.applications.van.MADE.dtype:1
#: tensorcircuit.applications.van.MaskedConv2D.dtype:1
#: tensorcircuit.applications.van.MaskedLinear.dtype:1
#: tensorcircuit.applications.van.NMF.dtype:1
#: tensorcircuit.applications.van.PixelCNN.dtype:1
#: tensorcircuit.applications.van.ResidualBlock.dtype:1
#: tensorcircuit.applications.vqes.Linear.dtype:1
#: tensorcircuit.keras.HardwareLayer.dtype:1
#: tensorcircuit.keras.QuantumLayer.dtype:1
msgid "Alias of `layer.variable_dtype`."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.evaluate:1 of
msgid "Returns the loss value & metrics values for the model in test mode."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.evaluate:3 of
msgid "Computation is done in batches (see the `batch_size` arg.)"
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.evaluate:5
#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:3 of
msgid ""
"Input data. It can be: - A NumPy array (or array-like), or a list of "
"arrays (in case the model has multiple inputs). - A backend-native "
"tensor, or a list of tensors (in case the model has multiple inputs). - A"
" dict mapping input names to the corresponding array/tensors, if the "
"model has named inputs. - A `keras.utils.PyDataset` returning `(inputs, "
"targets)` or `(inputs, targets, sample_weights)`. - A `tf.data.Dataset` "
"yielding `(inputs, targets)` or `(inputs, targets, sample_weights)`. - A "
"`torch.utils.data.DataLoader` yielding `(inputs, targets)` or `(inputs, "
"targets, sample_weights)`. - A Python generator function yielding "
"`(inputs, targets)` or `(inputs, targets, sample_weights)`."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.evaluate:20
#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:18 of
msgid ""
"Target data. Like the input data `x`, it can be either NumPy array(s) or "
"backend-native tensor(s). If `x` is a `keras.utils.PyDataset`, "
"`tf.data.Dataset`, `torch.utils.data.DataLoader` or a Python generator "
"function, `y` should not be specified since targets will be obtained from"
" `x`."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.evaluate:26
#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.predict:30 of
msgid ""
"Integer or `None`. Number of samples per batch of computation. If "
"unspecified, `batch_size` will default to 32. Do not specify the "
"`batch_size` if your input data `x` is a `keras.utils.PyDataset`, "
"`tf.data.Dataset`, `torch.utils.data.DataLoader` or Python generator "
"function since they generate batches."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.evaluate:33
#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.predict:37 of
msgid ""
"`\"auto\"`, 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 ="
" single line. `\"auto\"` becomes 1 for most cases. Note that the progress"
" bar is not particularly useful when logged to a file, so `verbose=2` is "
"recommended when not running interactively (e.g. in a production "
"environment). Defaults to `\"auto\"`."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.evaluate:40
#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:95 of
msgid ""
"Optional NumPy array or tensor of weights for the training samples, used "
"for weighting the loss function (during training only). You can either "
"pass a flat (1D) NumPy array or tensor with the same length as the input "
"samples (1:1 mapping between weights and samples), or in the case of "
"temporal data, you can pass a 2D NumPy array or tensor with shape "
"`(samples, sequence_length)` to apply a different weight to every "
"timestep of every sample. This argument is not supported when `x` is a "
"`keras.utils.PyDataset`, `tf.data.Dataset`, `torch.utils.data.DataLoader`"
" or Python generator function. Instead, provide `sample_weights` as the "
"third element of `x`. Note that sample weighting does not apply to "
"metrics specified via the `metrics` argument in `compile()`. To apply "
"sample weighting to your metrics, you can specify them via the "
"`weighted_metrics` in `compile()` instead."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.evaluate:56 of
msgid ""
"Integer or `None`. Total number of steps (batches of samples) to draw "
"before declaring the evaluation round finished. If `steps` is `None`, it "
"will run until `x` is exhausted. In the case of an infinitely repeating "
"dataset, it will run indefinitely."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.evaluate:61 of
msgid ""
"List of `keras.callbacks.Callback` instances. List of callbacks to apply "
"during evaluation."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.evaluate:63
#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.test_on_batch:10
#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.train_on_batch:18 of
msgid ""
"If `True`, loss and metric results are returned as a dict, with each key "
"being the name of the metric. If `False`, they are returned as a list."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.evaluate:67 of
msgid ""
"Scalar test loss (if the model has a single output and no metrics) or "
"list of scalars (if the model has multiple outputs and/or metrics)."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.evaluate:71 of
msgid ""
"Note: When using compiled metrics, `evaluate()` may return multiple "
"submetric values, while `model.metrics_names` often lists only top-level "
"names (e.g., 'loss', 'compile_metrics'), leading to a length mismatch. "
"The order of the `evaluate()` output corresponds to the order of metrics "
"specified during `model.compile()`. You can use this order to map the "
"`evaluate()` results to the intended metric. `model.metrics_names` itself"
" will still return only the top-level names."
msgstr ""

#: keras.src.models.model.Model.export:1 of
msgid "Export the model as an artifact for inference."
msgstr ""

#: keras.src.models.model.Model.export:3 of
msgid "`str` or `pathlib.Path` object. The path to save the artifact."
msgstr ""

#: keras.src.models.model.Model.export:5 of
msgid ""
"`str`. The export format. Supported values: `\"tf_saved_model\"`, "
"`\"onnx\"`, `\"openvino\"`, and `\"litert\"`. Defaults to "
"`\"tf_saved_model\"`."
msgstr ""

#: keras.src.models.model.Model.export:8 of
msgid ""
"`bool`. Whether to print a message during export. Defaults to `None`, "
"which uses the default value set by different backends and formats."
msgstr ""

#: keras.src.models.model.Model.export:11 of
msgid ""
"Optional. Specifies the shape and dtype of the model inputs. Can be a "
"structure of `keras.InputSpec`, `tf.TensorSpec`, `backend.KerasTensor`, "
"or backend tensor. If not provided, it will be automatically computed. "
"Defaults to `None`."
msgstr ""

#: keras.src.models.model.Model.export:16 of
msgid ""
"Additional keyword arguments. - `is_static`: Optional `bool`. Specific to"
" the JAX backend and     `format=\"tf_saved_model\"`. Indicates whether "
"`fn` is static.     Set to `False` if `fn` involves state updates (e.g., "
"RNG     seeds and counters). - `jax2tf_kwargs`: Optional `dict`. Specific"
" to the JAX backend     and `format=\"tf_saved_model\"`. Arguments for"
"     `jax2tf.convert`. See the documentation for     [`jax2tf.convert`]("
"         "
"https://github.com/google/jax/blob/main/jax/experimental/jax2tf/README.md)."
"     If `native_serialization` and `polymorphic_shapes` are not     "
"provided, they will be automatically computed. - `opset_version`: "
"Optional `int`. Specific to `format=\"onnx\"`.     An integer value that "
"specifies the ONNX opset version. - LiteRT-specific options: Optional "
"keyword arguments specific     to `format=\"litert\"`. These are passed "
"directly to the     TensorFlow Lite converter and include options like"
"     `optimizations`, `representative_dataset`,     "
"`experimental_new_quantizer`, `allow_custom_ops`,     "
"`enable_select_tf_ops`, etc. See TensorFlow Lite     documentation for "
"all available options."
msgstr ""

#: keras.src.models.model.Model.export:16 of
msgid ""
"Additional keyword arguments. - `is_static`: Optional `bool`. Specific to"
" the JAX backend and"
msgstr ""

#: keras.src.models.model.Model.export:18 of
msgid ""
"`format=\"tf_saved_model\"`. Indicates whether `fn` is static. Set to "
"`False` if `fn` involves state updates (e.g., RNG seeds and counters)."
msgstr ""

#: keras.src.models.model.Model.export:21 of
msgid "`jax2tf_kwargs`: Optional `dict`. Specific to the JAX backend"
msgstr ""

#: keras.src.models.model.Model.export:22 of
msgid ""
"and `format=\"tf_saved_model\"`. Arguments for `jax2tf.convert`. See the "
"documentation for [`jax2tf.convert`]("
msgstr ""

#: keras.src.models.model.Model.export:25 of
msgid "https://github.com/google/jax/blob/main/jax/experimental/jax2tf/README.md)."
msgstr ""

#: keras.src.models.model.Model.export:26 of
msgid ""
"If `native_serialization` and `polymorphic_shapes` are not provided, they"
" will be automatically computed."
msgstr ""

#: keras.src.models.model.Model.export:28 of
msgid "`opset_version`: Optional `int`. Specific to `format=\"onnx\"`."
msgstr ""

#: keras.src.models.model.Model.export:29 of
msgid "An integer value that specifies the ONNX opset version."
msgstr ""

#: keras.src.models.model.Model.export:30 of
msgid "LiteRT-specific options: Optional keyword arguments specific"
msgstr ""

#: keras.src.models.model.Model.export:31 of
msgid ""
"to `format=\"litert\"`. These are passed directly to the TensorFlow Lite "
"converter and include options like `optimizations`, "
"`representative_dataset`, `experimental_new_quantizer`, "
"`allow_custom_ops`, `enable_select_tf_ops`, etc. See TensorFlow Lite "
"documentation for all available options."
msgstr ""

#: keras.src.models.model.Model.export:38 of
msgid ""
"**Note:** This feature is currently supported only with TensorFlow, JAX "
"and Torch backends."
msgstr ""

#: keras.src.models.model.Model.export:41 of
msgid ""
"**Note:** Be aware that the exported artifact may contain information "
"from the local file system when using `format=\"onnx\"`, `verbose=True` "
"and Torch backend."
msgstr ""

#: keras.src.models.model.Model.export:45 of
msgid "Examples:"
msgstr ""

#: keras.src.models.model.Model.export:47 of
msgid "Here's how to export a TensorFlow SavedModel for inference."
msgstr ""

#: keras.src.models.model.Model.export:49 of
msgid ""
"```python # Export the model as a TensorFlow SavedModel artifact "
"model.export(\"path/to/location\", format=\"tf_saved_model\")"
msgstr ""

#: keras.src.models.model.Model.export:53 of
msgid ""
"# Load the artifact in a different process/environment reloaded_artifact "
"= tf.saved_model.load(\"path/to/location\") predictions = "
"reloaded_artifact.serve(input_data) ```"
msgstr ""

#: keras.src.models.model.Model.export:58 of
msgid "Here's how to export an ONNX for inference."
msgstr ""

#: keras.src.models.model.Model.export:60 of
msgid ""
"```python # Export the model as a ONNX artifact "
"model.export(\"path/to/location\", format=\"onnx\")"
msgstr ""

#: keras.src.models.model.Model.export:64 of
msgid ""
"# Load the artifact in a different process/environment ort_session = "
"onnxruntime.InferenceSession(\"path/to/location\") ort_inputs = {"
msgstr ""

#: keras.src.models.model.Model.export:67 of
msgid "k.name: v for k, v in zip(ort_session.get_inputs(), input_data)"
msgstr ""

#: keras.src.models.model.Model.export:68 of
msgid "} predictions = ort_session.run(None, ort_inputs) ```"
msgstr ""

#: keras.src.models.model.Model.export:72 of
msgid "Here's how to export a LiteRT (TFLite) for inference."
msgstr ""

#: keras.src.models.model.Model.export:74 of
msgid ""
"```python # Export the model as a LiteRT artifact "
"model.export(\"path/to/location\", format=\"litert\")"
msgstr ""

#: keras.src.models.model.Model.export:78 of
msgid ""
"# Load the artifact in a different process/environment interpreter = "
"tf.lite.Interpreter(model_path=\"path/to/location\") "
"interpreter.allocate_tensors() interpreter.set_tensor("
msgstr ""

#: keras.src.models.model.Model.export:82 of
msgid "interpreter.get_input_details()[0]['index'], input_data"
msgstr ""

#: keras.src.models.model.Model.export:83 of
msgid ") interpreter.invoke() output_data = interpreter.get_tensor("
msgstr ""

#: keras.src.models.model.Model.export:86 of
msgid "interpreter.get_output_details()[0]['index']"
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:1 of
msgid "Trains the model for a fixed number of epochs (dataset iterations)."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:24 of
msgid ""
"Integer or `None`. Number of samples per gradient update. If unspecified,"
" `batch_size` will default to 32. Do not specify the `batch_size` if your"
" input data `x` is a `keras.utils.PyDataset`, `tf.data.Dataset`, "
"`torch.utils.data.DataLoader` or Python generator function since they "
"generate batches."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:31 of
msgid ""
"Integer. Number of epochs to train the model. An epoch is an iteration "
"over the entire `x` and `y` data provided (unless the `steps_per_epoch` "
"flag is set to something other than None). Note that in conjunction with "
"`initial_epoch`, `epochs` is to be understood as \"final epoch\". The "
"model is not trained for a number of iterations given by `epochs`, but "
"merely until the epoch of index `epochs` is reached."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:41 of
msgid ""
"`\"auto\"`, 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 ="
" one line per epoch. \"auto\" becomes 1 for most cases. Note that the "
"progress bar is not particularly useful when logged to a file, so "
"`verbose=2` is recommended when not running interactively (e.g., in a "
"production environment). Defaults to `\"auto\"`."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:48 of
msgid ""
"List of `keras.callbacks.Callback` instances. List of callbacks to apply "
"during training. See `keras.callbacks`. Note "
"`keras.callbacks.ProgbarLogger` and `keras.callbacks.History` callbacks "
"are created automatically and need not be passed to `model.fit()`. "
"`keras.callbacks.ProgbarLogger` is created or not based on the `verbose` "
"argument in `model.fit()`."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:56 of
msgid ""
"Float between 0 and 1. Fraction of the training data to be used as "
"validation data. The model will set apart this fraction of the training "
"data, will not train on it, and will evaluate the loss and any model "
"metrics on this data at the end of each epoch. The validation data is "
"selected from the last samples in the `x` and `y` data provided, before "
"shuffling. This argument is only supported when `x` and `y` are made of "
"NumPy arrays or tensors. If both `validation_data` and `validation_split`"
" are provided, `validation_data` will override `validation_split`."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:67 of
msgid ""
"Data on which to evaluate the loss and any model metrics at the end of "
"each epoch. The model will not be trained on this data. Thus, note the "
"fact that the validation loss of data provided using `validation_split` "
"or `validation_data` is not affected by regularization layers like noise "
"and dropout. `validation_data` will override `validation_split`. It can "
"be: - A tuple `(x_val, y_val)` of NumPy arrays or tensors. - A tuple "
"`(x_val, y_val, val_sample_weights)` of NumPy arrays. - A "
"`keras.utils.PyDataset`, a `tf.data.Dataset`, a "
"`torch.utils.data.DataLoader` yielding `(inputs, targets)` or a Python "
"generator function yielding `(x_val, y_val)` or `(inputs, targets, "
"sample_weights)`."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:82 of
msgid ""
"Boolean, whether to shuffle the training data before each epoch. This "
"argument is ignored when `x` is a `keras.utils.PyDataset`, "
"`tf.data.Dataset`, `torch.utils.data.DataLoader` or Python generator "
"function."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:86 of
msgid ""
"Optional dictionary mapping class indices (integers) to a weight (float) "
"value, used for weighting the loss function (during training only). This "
"can be useful to tell the model to \"pay more attention\" to samples from"
" an under-represented class. When `class_weight` is specified and targets"
" have a rank of 2 or greater, either `y` must be one-hot encoded, or an "
"explicit final dimension of `1` must be included for sparse class labels."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:111 of
msgid ""
"Integer. Epoch at which to start training (useful for resuming a previous"
" training run)."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:114 of
msgid ""
"Integer or `None`. Total number of steps (batches of samples) before "
"declaring one epoch finished and starting the next epoch. When training "
"with input tensors or NumPy arrays, the default `None` means that the "
"value used is the number of samples in your dataset divided by the batch "
"size, or 1 if that cannot be determined. If `x` is a "
"`keras.utils.PyDataset`, `tf.data.Dataset`, `torch.utils.data.DataLoader`"
" or Python generator function, the epoch will run until the input dataset"
" is exhausted. When passing an infinitely repeating dataset, you must "
"specify the `steps_per_epoch` argument, otherwise the training will run "
"indefinitely."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:126 of
msgid ""
"Integer or `None`. Only relevant if `validation_data` is provided. Total "
"number of steps (batches of samples) to draw before stopping when "
"performing validation at the end of every epoch. If `validation_steps` is"
" `None`, validation will run until the `validation_data` dataset is "
"exhausted. In the case of an infinitely repeating dataset, it will run "
"indefinitely. If `validation_steps` is specified and only part of the "
"dataset is consumed, the evaluation will start from the beginning of the "
"dataset at each epoch. This ensures that the same validation samples are "
"used every time."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:137 of
msgid ""
"Integer or `None`. Number of samples per validation batch. If "
"unspecified, will default to `batch_size`. Do not specify the "
"`validation_batch_size` if your data is a `keras.utils.PyDataset`, "
"`tf.data.Dataset`, `torch.utils.data.DataLoader` or Python generator "
"function since they generate batches."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:144 of
msgid ""
"Only relevant if validation data is provided. Specifies how many training"
" epochs to run before a new validation run is performed, e.g. "
"`validation_freq=2` runs validation every 2 epochs."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:149 of
msgid "Unpacking behavior for iterator-like inputs:"
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:150 of
#, python-brace-format
msgid ""
"A common pattern is to pass an iterator like object such as a "
"`tf.data.Dataset` or a `keras.utils.PyDataset` to `fit()`, which will in "
"fact yield not only features (`x`) but optionally targets (`y`) and "
"sample weights (`sample_weight`). Keras requires that the output of such "
"iterator-likes be unambiguous. The iterator should return a tuple of "
"length 1, 2, or 3, where the optional second and third elements will be "
"used for `y` and `sample_weight` respectively. Any other type provided "
"will be wrapped in a length-one tuple, effectively treating everything as"
" `x`. When yielding dicts, they should still adhere to the top-level "
"tuple structure, e.g. `({\"x0\": x0, \"x1\": x1}, y)`. Keras will not "
"attempt to separate features, targets, and weights from the keys of a "
"single dict. A notable unsupported data type is the `namedtuple`. The "
"reason is that it behaves like both an ordered datatype (tuple) and a "
"mapping datatype (dict). So given a namedtuple of the form: "
"`namedtuple(\"example_tuple\", [\"y\", \"x\"])` it is ambiguous whether "
"to reverse the order of the elements when interpreting the value. Even "
"worse is a tuple of the form: `namedtuple(\"other_tuple\", [\"x\", \"y\","
" \"z\"])` where it is unclear if the tuple was intended to be unpacked "
"into `x`, `y`, and `sample_weight` or passed through as a single element "
"to `x`."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.fit:175 of
msgid ""
"A `History` object. Its `History.history` attribute is a record of "
"training loss values and metrics values at successive epochs, as well as "
"validation loss values and validation metrics values (if applicable)."
msgstr ""

#: keras.src.models.model.Model.from_config:1
#: keras.src.ops.operation.Operation.from_config:1 of
msgid "Creates an operation from its config."
msgstr ""

#: keras.src.models.model.Model.from_config:3
#: keras.src.ops.operation.Operation.from_config:3 of
msgid ""
"This method is the reverse of `get_config`, capable of instantiating the "
"same operation from the config dictionary."
msgstr ""

#: keras.src.models.model.Model.from_config:6
#: keras.src.ops.operation.Operation.from_config:6 of
msgid ""
"Note: If you override this method, you might receive a serialized dtype "
"config, which is a `dict`. You can deserialize it as follows:"
msgstr ""

#: keras.src.models.model.Model.from_config:9
#: keras.src.ops.operation.Operation.from_config:9 of
msgid "```python if \"dtype\" in config and isinstance(config[\"dtype\"], dict):"
msgstr ""

#: keras.src.models.model.Model.from_config:11
#: keras.src.ops.operation.Operation.from_config:11 of
msgid "policy = dtype_policies.deserialize(config[\"dtype\"])"
msgstr ""

#: keras.src.models.model.Model.from_config:14
#: keras.src.ops.operation.Operation.from_config:14 of
msgid "A Python dictionary, typically the output of `get_config`."
msgstr ""

#: keras.src.models.model.Model.from_config:16
#: keras.src.ops.operation.Operation.from_config:16 of
msgid "An operation instance."
msgstr ""

#: keras.src.layers.layer.Layer.get_build_config:1 of
msgid "Returns a dictionary with the layer's input shape."
msgstr ""

#: keras.src.layers.layer.Layer.get_build_config:3 of
msgid ""
"This method returns a config dict that can be used by "
"`build_from_config(config)` to create all states (e.g. Variables and "
"Lookup tables) needed by the layer."
msgstr ""

#: keras.src.layers.layer.Layer.get_build_config:7 of
msgid ""
"By default, the config only contains the input shape that the layer was "
"built with. If you're writing a custom layer that creates state in an "
"unusual way, you should override this method to make sure this state is "
"already created when Keras attempts to load its value upon model loading."
msgstr ""

#: keras.src.layers.layer.Layer.get_build_config:13 of
msgid "A dict containing the input shape associated with the layer."
msgstr ""

#: keras.src.trainers.trainer.Trainer.get_compile_config:1 of
msgid "Returns a serialized config with information for compiling the model."
msgstr ""

#: keras.src.trainers.trainer.Trainer.get_compile_config:3 of
msgid ""
"This method returns a config dictionary containing all the information "
"(optimizer, loss, metrics, etc.) with which the model was compiled."
msgstr ""

#: keras.src.trainers.trainer.Trainer.get_compile_config:6 of
msgid "A dict containing information for compiling the model."
msgstr ""

#: keras.src.layers.layer.Layer.get_config:1 of
msgid "Returns the config of the object."
msgstr ""

#: keras.src.layers.layer.Layer.get_config:3 of
msgid ""
"An object config is a Python dictionary (serializable) containing the "
"information needed to re-instantiate it."
msgstr ""

#: keras.src.models.model.Model.get_layer:1 of
msgid "Retrieves a layer based on either its name (unique) or index."
msgstr ""

#: keras.src.models.model.Model.get_layer:3 of
msgid ""
"If `name` and `index` are both provided, `index` will take precedence. "
"Indices are based on order of horizontal graph traversal (bottom-up)."
msgstr ""

#: keras.src.models.model.Model.get_layer:6 of
msgid "String, name of layer."
msgstr ""

#: keras.src.models.model.Model.get_layer:7 of
msgid "Integer, index of layer."
msgstr ""

#: keras.src.models.model.Model.get_layer:9 of
msgid "A layer instance."
msgstr ""

#: keras.src.trainers.trainer.Trainer.get_metrics_result:1 of
msgid "Returns the model's metrics values as a dict."
msgstr ""

#: keras.src.trainers.trainer.Trainer.get_metrics_result:3 of
msgid ""
"If any of the metric result is a dict (containing multiple metrics), each"
" of them gets added to the top level returned dict of this method."
msgstr ""

#: keras.src.trainers.trainer.Trainer.get_metrics_result:6 of
#, python-brace-format
msgid ""
"A `dict` containing values of the metrics listed in `self.metrics`. "
"Example: `{'loss': 0.2, 'accuracy': 0.7}`."
msgstr ""

#: keras.src.models.model.Model.get_quantization_layer_structure:1 of
msgid "Returns the quantization structure for the model."
msgstr ""

#: keras.src.models.model.Model.get_quantization_layer_structure:3 of
msgid ""
"This method is intended to be overridden by model authors to provide "
"topology information required for structure-aware quantization modes like"
" 'gptq'."
msgstr ""

#: keras.src.models.model.Model.get_quantization_layer_structure:7 of
msgid "The quantization mode."
msgstr ""

#: keras.src.models.model.Model.get_quantization_layer_structure:9 of
#, python-brace-format
msgid ""
"`{'pre_block_layers': [list], 'sequential_blocks': [list]}` or `None` if "
"the mode does not require structure or is not supported. "
"`'pre_block_layers'` is a list of layers that the inputs should be passed"
" through, before being passed to the sequential blocks. For example, "
"inputs to an LLM must first be passed through an embedding layer, "
"followed by the transformer."
msgstr ""

#: keras.src.models.model.Model.get_state_tree:1 of
msgid "Retrieves tree-like structure of model variables."
msgstr ""

#: keras.src.models.model.Model.get_state_tree:3 of
msgid ""
"This method allows retrieval of different model variables (trainable, "
"non-trainable, optimizer, and metrics). The variables are returned in a "
"nested dictionary format, where the keys correspond to the variable names"
" and the values are the nested representations of the variables."
msgstr ""

#: keras.src.models.model.Model.get_state_tree:8 of
msgid ""
"A dictionary containing the nested representations of the     requested "
"variables. The keys are the variable names, and the     values are the "
"corresponding nested dictionaries. value_format: One of "
"`\"backend_tensor\"`, `\"numpy_array\"`.     The kind of array to return "
"as the leaves of the nested         state tree."
msgstr ""

#: keras.src.models.model.Model.get_state_tree:10 of
msgid "A dictionary containing the nested representations of the"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:11 of
msgid ""
"requested variables. The keys are the variable names, and the values are "
"the corresponding nested dictionaries."
msgstr ""

#: keras.src.models.model.Model.get_state_tree:13 of
msgid "value_format: One of `\"backend_tensor\"`, `\"numpy_array\"`."
msgstr ""

#: keras.src.models.model.Model.get_state_tree:14 of
msgid "The kind of array to return as the leaves of the nested"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:15 of
msgid "state tree."
msgstr ""

#: keras.src.models.model.Model.get_state_tree:20 of
msgid "```python model = keras.Sequential(["
msgstr ""

#: keras.src.models.model.Model.get_state_tree:22 of
msgid ""
"keras.Input(shape=(1,), name=\"my_input\"), keras.layers.Dense(1, "
"activation=\"sigmoid\", name=\"my_dense\"),"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:24 of
msgid ""
"], name=\"my_sequential\") model.compile(optimizer=\"adam\", "
"loss=\"mse\", metrics=[\"mae\"]) model.fit(np.array([[1.0]]), "
"np.array([[1.0]])) state_tree = model.get_state_tree() ```"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:30 of
msgid "The `state_tree` dictionary returned looks like:"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:32 of
msgid "``` {"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:34 of
msgid "'metrics_variables': {"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:35 of
msgid "'loss': {"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:36
#: keras.src.models.model.Model.get_state_tree:40 of
msgid "'count': ..., 'total': ...,"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:38 of
msgid "}, 'mean_absolute_error': {"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:42
#: keras.src.models.model.Model.get_state_tree:49
#: keras.src.models.model.Model.get_state_tree:50
#: keras.src.models.model.Model.get_state_tree:61
#: keras.src.models.model.Model.get_state_tree:62
#: keras.src.models.model.Model.get_state_tree:63
#: keras.src.models.model.Model.get_state_tree:65 of
msgid "}"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:43 of
msgid "}, 'trainable_variables': {"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:45 of
msgid "'my_sequential': {"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:46 of
msgid "'my_dense': {"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:47 of
msgid "'bias': ..., 'kernel': ...,"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:51 of
msgid "}, 'non_trainable_variables': {}, 'optimizer_variables': {"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:54 of
msgid "'adam': {"
msgstr ""

#: keras.src.models.model.Model.get_state_tree:55 of
msgid ""
"'iteration': ..., 'learning_rate': ..., "
"'my_sequential_my_dense_bias_momentum': ..., "
"'my_sequential_my_dense_bias_velocity': ..., "
"'my_sequential_my_dense_kernel_momentum': ..., "
"'my_sequential_my_dense_kernel_velocity': ...,"
msgstr ""

#: keras.src.layers.layer.Layer.get_weights:1 of
msgid "Return the values of `layer.weights` as a list of NumPy arrays."
msgstr ""

#: of tensorcircuit.applications.van.MADE.input:1
#: tensorcircuit.applications.van.MaskedConv2D.input:1
#: tensorcircuit.applications.van.MaskedLinear.input:1
#: tensorcircuit.applications.van.NMF.input:1
#: tensorcircuit.applications.van.PixelCNN.input:1
#: tensorcircuit.applications.van.ResidualBlock.input:1
#: tensorcircuit.applications.vqes.Linear.input:1
#: tensorcircuit.keras.HardwareLayer.input:1
#: tensorcircuit.keras.QuantumLayer.input:1
msgid "Retrieves the input tensor(s) of a symbolic operation."
msgstr ""

#: of tensorcircuit.applications.van.MADE.input:3
#: tensorcircuit.applications.van.MADE.output:3
#: tensorcircuit.applications.van.MaskedConv2D.input:3
#: tensorcircuit.applications.van.MaskedConv2D.output:3
#: tensorcircuit.applications.van.MaskedLinear.input:3
#: tensorcircuit.applications.van.MaskedLinear.output:3
#: tensorcircuit.applications.van.NMF.input:3
#: tensorcircuit.applications.van.NMF.output:3
#: tensorcircuit.applications.van.PixelCNN.input:3
#: tensorcircuit.applications.van.PixelCNN.output:3
#: tensorcircuit.applications.van.ResidualBlock.input:3
#: tensorcircuit.applications.van.ResidualBlock.output:3
#: tensorcircuit.applications.vqes.Linear.input:3
#: tensorcircuit.applications.vqes.Linear.output:3
#: tensorcircuit.keras.HardwareLayer.input:3
#: tensorcircuit.keras.HardwareLayer.output:3
#: tensorcircuit.keras.QuantumLayer.input:3
#: tensorcircuit.keras.QuantumLayer.output:3
msgid ""
"Only returns the tensor(s) corresponding to the *first time* the "
"operation was called."
msgstr ""

#: of tensorcircuit.applications.van.MADE.input:6
#: tensorcircuit.applications.van.MaskedConv2D.input:6
#: tensorcircuit.applications.van.MaskedLinear.input:6
#: tensorcircuit.applications.van.NMF.input:6
#: tensorcircuit.applications.van.PixelCNN.input:6
#: tensorcircuit.applications.van.ResidualBlock.input:6
#: tensorcircuit.applications.vqes.Linear.input:6
#: tensorcircuit.keras.HardwareLayer.input:6
#: tensorcircuit.keras.QuantumLayer.input:6
msgid "Input tensor or list of input tensors."
msgstr ""

#: of tensorcircuit.applications.van.MADE.input_dtype:1
#: tensorcircuit.applications.van.MaskedConv2D.input_dtype:1
#: tensorcircuit.applications.van.MaskedLinear.input_dtype:1
#: tensorcircuit.applications.van.NMF.input_dtype:1
#: tensorcircuit.applications.van.PixelCNN.input_dtype:1
#: tensorcircuit.applications.van.ResidualBlock.input_dtype:1
#: tensorcircuit.applications.vqes.Linear.input_dtype:1
#: tensorcircuit.keras.HardwareLayer.input_dtype:1
#: tensorcircuit.keras.QuantumLayer.input_dtype:1
msgid "The dtype layer inputs should be converted to."
msgstr ""

#: keras.src.layers.layer.Layer.load_own_variables:1 of
msgid "Loads the state of the layer."
msgstr ""

#: keras.src.layers.layer.Layer.load_own_variables:3 of
msgid ""
"You can override this method to take full control of how the state of the"
" layer is loaded upon calling `keras.models.load_model()`."
msgstr ""

#: keras.src.layers.layer.Layer.load_own_variables:6 of
msgid "Dict from which the state of the model will be loaded."
msgstr ""

#: keras.src.models.model.Model.load_weights:1 of
msgid "Load the weights from a single file or sharded files."
msgstr ""

#: keras.src.models.model.Model.load_weights:3 of
msgid ""
"Weights are loaded based on the network's topology. This means the "
"architecture should be the same as when the weights were saved. Note that"
" layers that don't have weights are not taken into account in the "
"topological ordering, so adding or removing layers is fine as long as "
"they don't have weights."
msgstr ""

#: keras.src.models.model.Model.load_weights:9 of
msgid "**Partial weight loading**"
msgstr ""

#: keras.src.models.model.Model.load_weights:11 of
msgid ""
"If you have modified your model, for instance by adding a new layer (with"
" weights) or by changing the shape of the weights of a layer, you can "
"choose to ignore errors and continue loading by setting "
"`skip_mismatch=True`. In this case any layer with mismatching weights "
"will be skipped. A warning will be displayed for each skipped layer."
msgstr ""

#: keras.src.models.model.Model.load_weights:17 of
msgid "**Sharding**"
msgstr ""

#: keras.src.models.model.Model.load_weights:19 of
msgid ""
"When loading sharded weights, it is important to specify `filepath` that "
"ends with `*.weights.json` which is used as the configuration file. "
"Additionally, the sharded files `*_xxxxx.weights.h5` must be in the same "
"directory as the configuration file."
msgstr ""

#: keras.src.models.model.Model.load_weights:24 of
msgid ""
"`str` or `pathlib.Path` object. Path where the weights will be saved.  "
"When sharding, the filepath must end in `.weights.json`."
msgstr ""

#: keras.src.models.model.Model.load_weights:27 of
msgid ""
"Boolean, whether to skip loading of layers where there is a mismatch in "
"the number of weights, or a mismatch in the shape of the weights."
msgstr ""

#: keras.src.models.model.Model.load_weights:33 of
msgid ""
"```python # Load the weights in a single file. "
"model.load_weights(\"model.weights.h5\")"
msgstr ""

#: keras.src.models.model.Model.load_weights:37 of
msgid ""
"# Load the weights in sharded files. "
"model.load_weights(\"model.weights.json\") ```"
msgstr ""

#: of tensorcircuit.applications.van.MADE.losses:1
#: tensorcircuit.applications.van.MaskedConv2D.losses:1
#: tensorcircuit.applications.van.MaskedLinear.losses:1
#: tensorcircuit.applications.van.NMF.losses:1
#: tensorcircuit.applications.van.PixelCNN.losses:1
#: tensorcircuit.applications.van.ResidualBlock.losses:1
#: tensorcircuit.applications.vqes.Linear.losses:1
#: tensorcircuit.keras.HardwareLayer.losses:1
#: tensorcircuit.keras.QuantumLayer.losses:1
msgid "List of scalar losses from `add_loss`, regularizers and sublayers."
msgstr ""

#: of tensorcircuit.applications.van.MADE.metrics:1
#: tensorcircuit.applications.van.MaskedConv2D.metrics:1
#: tensorcircuit.applications.van.MaskedLinear.metrics:1
#: tensorcircuit.applications.van.NMF.metrics:1
#: tensorcircuit.applications.van.PixelCNN.metrics:1
#: tensorcircuit.applications.van.ResidualBlock.metrics:1
#: tensorcircuit.applications.vqes.Linear.metrics:1
#: tensorcircuit.keras.HardwareLayer.metrics:1
#: tensorcircuit.keras.QuantumLayer.metrics:1
msgid "List of all metrics."
msgstr ""

#: of tensorcircuit.applications.van.MADE.metrics_variables:1
#: tensorcircuit.applications.van.MaskedConv2D.metrics_variables:1
#: tensorcircuit.applications.van.MaskedLinear.metrics_variables:1
#: tensorcircuit.applications.van.NMF.metrics_variables:1
#: tensorcircuit.applications.van.PixelCNN.metrics_variables:1
#: tensorcircuit.applications.van.ResidualBlock.metrics_variables:1
#: tensorcircuit.applications.vqes.Linear.metrics_variables:1
#: tensorcircuit.keras.HardwareLayer.metrics_variables:1
#: tensorcircuit.keras.QuantumLayer.metrics_variables:1
msgid "List of all metric variables."
msgstr ""

#: of tensorcircuit.applications.van.MADE.non_trainable_variables:1
#: tensorcircuit.applications.van.MaskedConv2D.non_trainable_variables:1
#: tensorcircuit.applications.van.MaskedLinear.non_trainable_variables:1
#: tensorcircuit.applications.van.NMF.non_trainable_variables:1
#: tensorcircuit.applications.van.PixelCNN.non_trainable_variables:1
#: tensorcircuit.applications.van.ResidualBlock.non_trainable_variables:1
#: tensorcircuit.applications.vqes.Linear.non_trainable_variables:1
#: tensorcircuit.keras.HardwareLayer.non_trainable_variables:1
#: tensorcircuit.keras.QuantumLayer.non_trainable_variables:1
msgid "List of all non-trainable layer state."
msgstr ""

#: of tensorcircuit.applications.van.MADE.non_trainable_variables:3
#: tensorcircuit.applications.van.MaskedConv2D.non_trainable_variables:3
#: tensorcircuit.applications.van.MaskedLinear.non_trainable_variables:3
#: tensorcircuit.applications.van.NMF.non_trainable_variables:3
#: tensorcircuit.applications.van.PixelCNN.non_trainable_variables:3
#: tensorcircuit.applications.van.ResidualBlock.non_trainable_variables:3
#: tensorcircuit.applications.vqes.Linear.non_trainable_variables:3
#: tensorcircuit.keras.HardwareLayer.non_trainable_variables:3
#: tensorcircuit.keras.QuantumLayer.non_trainable_variables:3
msgid ""
"This extends `layer.non_trainable_weights` to include all state used by "
"the layer including state for metrics and `SeedGenerator`s."
msgstr ""

#: of tensorcircuit.applications.van.MADE.non_trainable_weights:1
#: tensorcircuit.applications.van.MaskedConv2D.non_trainable_weights:1
#: tensorcircuit.applications.van.MaskedLinear.non_trainable_weights:1
#: tensorcircuit.applications.van.NMF.non_trainable_weights:1
#: tensorcircuit.applications.van.PixelCNN.non_trainable_weights:1
#: tensorcircuit.applications.van.ResidualBlock.non_trainable_weights:1
#: tensorcircuit.applications.vqes.Linear.non_trainable_weights:1
#: tensorcircuit.keras.HardwareLayer.non_trainable_weights:1
#: tensorcircuit.keras.QuantumLayer.non_trainable_weights:1
msgid "List of all non-trainable weight variables of the layer."
msgstr ""

#: of tensorcircuit.applications.van.MADE.non_trainable_weights:3
#: tensorcircuit.applications.van.MaskedConv2D.non_trainable_weights:3
#: tensorcircuit.applications.van.MaskedLinear.non_trainable_weights:3
#: tensorcircuit.applications.van.NMF.non_trainable_weights:3
#: tensorcircuit.applications.van.PixelCNN.non_trainable_weights:3
#: tensorcircuit.applications.van.ResidualBlock.non_trainable_weights:3
#: tensorcircuit.applications.vqes.Linear.non_trainable_weights:3
#: tensorcircuit.keras.HardwareLayer.non_trainable_weights:3
#: tensorcircuit.keras.QuantumLayer.non_trainable_weights:3
msgid ""
"These are the weights that should not be updated by the optimizer during "
"training. Unlike, `layer.non_trainable_variables` this excludes metric "
"state and random seeds."
msgstr ""

#: of tensorcircuit.applications.van.MADE.output:1
#: tensorcircuit.applications.van.MaskedConv2D.output:1
#: tensorcircuit.applications.van.MaskedLinear.output:1
#: tensorcircuit.applications.van.NMF.output:1
#: tensorcircuit.applications.van.PixelCNN.output:1
#: tensorcircuit.applications.van.ResidualBlock.output:1
#: tensorcircuit.applications.vqes.Linear.output:1
#: tensorcircuit.keras.HardwareLayer.output:1
#: tensorcircuit.keras.QuantumLayer.output:1
msgid "Retrieves the output tensor(s) of a layer."
msgstr ""

#: of tensorcircuit.applications.van.MADE.output:6
#: tensorcircuit.applications.van.MaskedConv2D.output:6
#: tensorcircuit.applications.van.MaskedLinear.output:6
#: tensorcircuit.applications.van.NMF.output:6
#: tensorcircuit.applications.van.PixelCNN.output:6
#: tensorcircuit.applications.van.ResidualBlock.output:6
#: tensorcircuit.applications.vqes.Linear.output:6
#: tensorcircuit.keras.HardwareLayer.output:6
#: tensorcircuit.keras.QuantumLayer.output:6
msgid "Output tensor or list of output tensors."
msgstr ""

#: of tensorcircuit.applications.van.MADE.path:1
#: tensorcircuit.applications.van.MaskedConv2D.path:1
#: tensorcircuit.applications.van.MaskedLinear.path:1
#: tensorcircuit.applications.van.NMF.path:1
#: tensorcircuit.applications.van.PixelCNN.path:1
#: tensorcircuit.applications.van.ResidualBlock.path:1
#: tensorcircuit.applications.vqes.Linear.path:1
#: tensorcircuit.keras.HardwareLayer.path:1
#: tensorcircuit.keras.QuantumLayer.path:1
msgid "The path of the layer."
msgstr ""

#: of tensorcircuit.applications.van.MADE.path:3
#: tensorcircuit.applications.van.MaskedConv2D.path:3
#: tensorcircuit.applications.van.MaskedLinear.path:3
#: tensorcircuit.applications.van.NMF.path:3
#: tensorcircuit.applications.van.PixelCNN.path:3
#: tensorcircuit.applications.van.ResidualBlock.path:3
#: tensorcircuit.applications.vqes.Linear.path:3
#: tensorcircuit.keras.HardwareLayer.path:3
#: tensorcircuit.keras.QuantumLayer.path:3
msgid "If the layer has not been built yet, it will be `None`."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.predict:1 of
msgid "Generates output predictions for the input samples."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.predict:3 of
msgid ""
"Computation is done in batches. This method is designed for batch "
"processing of large numbers of inputs. It is not intended for use inside "
"of loops that iterate over your data and process small numbers of inputs "
"at a time."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.predict:8 of
msgid ""
"For small numbers of inputs that fit in one batch, directly use "
"`__call__()` for faster execution, e.g., `model(x)`, or `model(x, "
"training=False)` if you have layers such as `BatchNormalization` that "
"behave differently during inference."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.predict:14 of
msgid ""
"Note: See [this FAQ entry]( https://keras.io/getting_started/faq/#whats-"
"the-difference-between-model-methods-predict-and-call) for more details "
"about the difference between `Model` methods `predict()` and "
"`__call__()`."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.predict:19 of
msgid ""
"Input data. It can be: - A NumPy array (or array-like), or a list of "
"arrays (in case the model has multiple inputs). - A backend-native "
"tensor, or a list of tensors (in case the model has multiple inputs). - A"
" dict mapping input names to the corresponding array/tensors, if the "
"model has named inputs. - A `keras.utils.PyDataset`. - A "
"`tf.data.Dataset`. - A `torch.utils.data.DataLoader`. - A Python "
"generator function."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.predict:43 of
msgid ""
"Total number of steps (batches of samples) to draw before declaring the "
"prediction round finished. If `steps` is `None`, it will run until `x` is"
" exhausted. In the case of an infinitely repeating dataset, it will run "
"indefinitely."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.predict:47 of
msgid ""
"List of `keras.callbacks.Callback` instances. List of callbacks to apply "
"during prediction."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.predict:50
#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.predict_on_batch:5 of
msgid "NumPy array(s) of predictions."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.predict_on_batch:1 of
msgid "Returns predictions for a single batch of samples."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.predict_on_batch:3 of
msgid "Input data. It must be array-like."
msgstr ""

#: of tensorcircuit.applications.van.MADE.quantization_mode:1
#: tensorcircuit.applications.van.MaskedConv2D.quantization_mode:1
#: tensorcircuit.applications.van.MaskedLinear.quantization_mode:1
#: tensorcircuit.applications.van.NMF.quantization_mode:1
#: tensorcircuit.applications.van.PixelCNN.quantization_mode:1
#: tensorcircuit.applications.van.ResidualBlock.quantization_mode:1
#: tensorcircuit.applications.vqes.Linear.quantization_mode:1
#: tensorcircuit.keras.HardwareLayer.quantization_mode:1
#: tensorcircuit.keras.QuantumLayer.quantization_mode:1
msgid "The quantization mode of this layer, `None` if not quantized."
msgstr ""

#: keras.src.models.model.Model.quantize:1 of
msgid "Quantize the weights of the model."
msgstr ""

#: keras.src.models.model.Model.quantize:3 of
msgid ""
"Note that the model must be built first before calling this method. "
"`quantize` will recursively call `quantize(...)` in all layers and will "
"be skipped if the layer doesn't implement the function."
msgstr ""

#: keras.src.models.model.Model.quantize:7 of
msgid ""
"This method can be called by passing a `mode` string, which uses the "
"default configuration for that mode. Alternatively, a `config` object can"
" be passed to customize the behavior of the quantization (e.g. to use "
"specific quantizers for weights or activations)."
msgstr ""

#: keras.src.models.model.Model.quantize:12 of
msgid ""
"The mode of the quantization. Supported modes are: `\"int8\"`, "
"`\"int4\"`, `\"float8\"`, `\"gptq\"`. This is optional if `config` is "
"provided."
msgstr ""

#: keras.src.models.model.Model.quantize:15 of
msgid ""
"The configuration object specifying additional quantization options. This"
" argument allows to configure the weight and activation quantizers. be an"
" instance of `keras.quantizers.QuantizationConfig`."
msgstr ""

#: keras.src.models.model.Model.quantize:19 of
msgid ""
"Optional filters to apply to the quantization. Can be a regex string, a "
"list of regex strings, or a callable. Only the layers which match the "
"filter conditions will be quantized."
msgstr ""

#: keras.src.models.model.Model.quantize:22 of
msgid "Additional keyword arguments."
msgstr ""

#: keras.src.models.model.Model.quantize:26 of
msgid "Quantize a model to int8 with default configuration:"
msgstr ""

#: keras.src.models.model.Model.quantize:28 of
msgid "```python # Build the model model = keras.Sequential(["
msgstr ""

#: keras.src.models.model.Model.quantize:31
#: keras.src.models.model.Model.quantize:48 of
msgid "keras.Input(shape=(10,)), keras.layers.Dense(10),"
msgstr ""

#: keras.src.models.model.Model.quantize:33
#: keras.src.models.model.Model.quantize:50 of
msgid "]) model.build((None, 10))"
msgstr ""

#: keras.src.models.model.Model.quantize:36 of
msgid "# Quantize with default int8 config model.quantize(\"int8\") ```"
msgstr ""

#: keras.src.models.model.Model.quantize:40 of
msgid "Quantize a model to int8 with a custom configuration:"
msgstr ""

#: keras.src.models.model.Model.quantize:42 of
msgid ""
"```python from keras.quantizers import Int8QuantizationConfig from "
"keras.quantizers import AbsMaxQuantizer"
msgstr ""

#: keras.src.models.model.Model.quantize:46 of
msgid "# Build the model model = keras.Sequential(["
msgstr ""

#: keras.src.models.model.Model.quantize:53 of
msgid "# Create a custom config config = Int8QuantizationConfig("
msgstr ""

#: keras.src.models.model.Model.quantize:55 of
msgid "weight_quantizer=AbsMaxQuantizer("
msgstr ""

#: keras.src.models.model.Model.quantize:56 of
msgid "axis=0, value_range=(-127, 127)"
msgstr ""

#: keras.src.models.model.Model.quantize:58 of
msgid "), activation_quantizer=AbsMaxQuantizer("
msgstr ""

#: keras.src.models.model.Model.quantize:60 of
msgid "axis=-1, value_range=(-127, 127)"
msgstr ""

#: keras.src.models.model.Model.quantize:62 of
msgid "),"
msgstr ""

#: keras.src.models.model.Model.quantize:65 of
msgid "# Quantize with custom config model.quantize(config=config) ```"
msgstr ""

#: keras.src.layers.layer.Layer.rematerialized_call:1 of
msgid "Enable rematerialization dynamically for layer's call method."
msgstr ""

#: keras.src.layers.layer.Layer.rematerialized_call:3 of
msgid "The original `call` method of a layer."
msgstr ""

#: keras.src.layers.layer.Layer.rematerialized_call:5 of
msgid "Rematerialized layer's `call` method."
msgstr ""

#: keras.src.models.model.Model.save:1 of
msgid "Saves a model as a `.keras` file."
msgstr ""

#: keras.src.models.model.Model.save:3 of
msgid "Note that `model.save()` is an alias for `keras.saving.save_model()`."
msgstr ""

#: keras.src.models.model.Model.save:5 of
msgid "The saved `.keras` file contains:"
msgstr ""

#: keras.src.models.model.Model.save:7 of
msgid "The model's configuration (architecture)"
msgstr ""

#: keras.src.models.model.Model.save:8 of
msgid "The model's weights"
msgstr ""

#: keras.src.models.model.Model.save:9 of
msgid "The model's optimizer's state (if any)"
msgstr ""

#: keras.src.models.model.Model.save:11 of
msgid "Thus models can be reinstantiated in the exact same state."
msgstr ""

#: keras.src.models.model.Model.save:13 of
msgid ""
"`str` or `pathlib.Path` object. The path where to save the model. Must "
"end in `.keras` (unless saving the model as an unzipped directory via "
"`zipped=False`)."
msgstr ""

#: keras.src.models.model.Model.save:17 of
msgid ""
"Whether we should overwrite any existing model at the target location, or"
" instead ask the user via an interactive prompt."
msgstr ""

#: keras.src.models.model.Model.save:20 of
msgid ""
"Whether to save the model as a zipped `.keras` archive (default when "
"saving locally), or as an unzipped directory (default when saving on the "
"Hugging Face Hub)."
msgstr ""

#: keras.src.models.model.Model.save:27 of
msgid "```python model = keras.Sequential("
msgstr ""

#: keras.src.models.model.Model.save:29 of
msgid "["
msgstr ""

#: keras.src.models.model.Model.save:30 of
msgid "keras.layers.Dense(5, input_shape=(3,)), keras.layers.Softmax(),"
msgstr ""

#: keras.src.models.model.Model.save:33 of
msgid ""
") model.save(\"model.keras\") loaded_model = "
"keras.saving.load_model(\"model.keras\") x = keras.random.uniform((10, "
"3)) assert np.allclose(model.predict(x), loaded_model.predict(x)) ```"
msgstr ""

#: keras.src.layers.layer.Layer.save_own_variables:1 of
msgid "Saves the state of the layer."
msgstr ""

#: keras.src.layers.layer.Layer.save_own_variables:3 of
msgid ""
"You can override this method to take full control of how the state of the"
" layer is saved upon calling `model.save()`."
msgstr ""

#: keras.src.layers.layer.Layer.save_own_variables:6 of
msgid "Dict where the state of the model will be saved."
msgstr ""

#: keras.src.models.model.Model.save_weights:1 of
msgid "Saves all weights to a single file or sharded files."
msgstr ""

#: keras.src.models.model.Model.save_weights:3 of
msgid ""
"By default, the weights will be saved in a single `.weights.h5` file. If "
"sharding is enabled (`max_shard_size` is not `None`), the weights will be"
" saved in multiple files, each with a size at most `max_shard_size` (in "
"GB). Additionally, a configuration file `.weights.json` will contain the "
"metadata for the sharded files."
msgstr ""

#: keras.src.models.model.Model.save_weights:9 of
msgid "The saved sharded files contain:"
msgstr ""

#: keras.src.models.model.Model.save_weights:11 of
msgid "`*.weights.json`: The configuration file containing 'metadata' and"
msgstr ""

#: keras.src.models.model.Model.save_weights:12 of
msgid "'weight_map'."
msgstr ""

#: keras.src.models.model.Model.save_weights:13 of
msgid "`*_xxxxxx.weights.h5`: The sharded files containing only the"
msgstr ""

#: keras.src.models.model.Model.save_weights:14 of
msgid "weights."
msgstr ""

#: keras.src.models.model.Model.save_weights:16 of
msgid ""
"`str` or `pathlib.Path` object. Path where the weights will be saved.  "
"When sharding, the filepath must end in `.weights.json`. If `.weights.h5`"
" is provided, it will be overridden."
msgstr ""

#: keras.src.models.model.Model.save_weights:20 of
msgid ""
"Whether to overwrite any existing weights at the target location or "
"instead ask the user via an interactive prompt."
msgstr ""

#: keras.src.models.model.Model.save_weights:22 of
msgid ""
"`int` or `float`. Maximum size in GB for each sharded file. If `None`, no"
" sharding will be done. Defaults to `None`."
msgstr ""

#: keras.src.models.model.Model.save_weights:28 of
msgid ""
"```python # Instantiate a EfficientNetV2L model with about 454MB of "
"weights. model = keras.applications.EfficientNetV2L(weights=None)"
msgstr ""

#: keras.src.models.model.Model.save_weights:32 of
msgid ""
"# Save the weights in a single file. "
"model.save_weights(\"model.weights.h5\")"
msgstr ""

#: keras.src.models.model.Model.save_weights:35 of
msgid ""
"# Save the weights in sharded files. Use `max_shard_size=0.25` means # "
"each sharded file will be at most ~250MB. "
"model.save_weights(\"model.weights.json\", max_shard_size=0.25)"
msgstr ""

#: keras.src.models.model.Model.save_weights:39 of
msgid ""
"# Load the weights in a new model with the same architecture. "
"loaded_model = keras.applications.EfficientNetV2L(weights=None) "
"loaded_model.load_weights(\"model.weights.h5\") x = "
"keras.random.uniform((1, 480, 480, 3)) assert "
"np.allclose(model.predict(x), loaded_model.predict(x))"
msgstr ""

#: keras.src.models.model.Model.save_weights:45 of
msgid ""
"# Load the sharded weights in a new model with the same architecture. "
"loaded_model = keras.applications.EfficientNetV2L(weights=None) "
"loaded_model.load_weights(\"model.weights.json\") x = "
"keras.random.uniform((1, 480, 480, 3)) assert "
"np.allclose(model.predict(x), loaded_model.predict(x)) ```"
msgstr ""

#: keras.src.models.model.Model.set_state_tree:1 of
msgid "Assigns values to variables of the model."
msgstr ""

#: keras.src.models.model.Model.set_state_tree:3 of
msgid ""
"This method takes a dictionary of nested variable values, which "
"represents the state tree of the model, and assigns them to the "
"corresponding variables of the model. The dictionary keys represent the "
"variable names (e.g., `'trainable_variables'`, `'optimizer_variables'`), "
"and the values are nested dictionaries containing the variable paths and "
"their corresponding values."
msgstr ""

#: keras.src.models.model.Model.set_state_tree:10 of
msgid ""
"A dictionary representing the state tree of the model. The keys are the "
"variable names, and the values are nested dictionaries representing the "
"variable paths and their values."
msgstr ""

#: keras.src.layers.layer.Layer.set_weights:1 of
msgid "Sets the values of `layer.weights` from a list of NumPy arrays."
msgstr ""

#: keras.src.layers.layer.Layer.stateless_call:1 of
msgid "Call the layer without any side effects."
msgstr ""

#: keras.src.layers.layer.Layer.stateless_call:3 of
msgid "List of trainable variables of the model."
msgstr ""

#: keras.src.layers.layer.Layer.stateless_call:4 of
msgid "List of non-trainable variables of the model."
msgstr ""

#: keras.src.layers.layer.Layer.stateless_call:6 of
msgid "Positional arguments to be passed to `call()`."
msgstr ""

#: keras.src.layers.layer.Layer.stateless_call:7 of
msgid ""
"If `True`, `stateless_call()` will return the list of losses created "
"during `call()` as part of its return values."
msgstr ""

#: keras.src.layers.layer.Layer.stateless_call:9 of
msgid "Keyword arguments to be passed to `call()`."
msgstr ""

#: keras.src.layers.layer.Layer.stateless_call:11 of
msgid ""
"A tuple. By default, returns `(outputs, non_trainable_variables)`.     If"
" `return_losses = True`, then returns     `(outputs, "
"non_trainable_variables, losses)`."
msgstr ""

#: keras.src.layers.layer.Layer.stateless_call:13 of
msgid "A tuple. By default, returns `(outputs, non_trainable_variables)`."
msgstr ""

#: keras.src.layers.layer.Layer.stateless_call:14 of
msgid ""
"If `return_losses = True`, then returns `(outputs, "
"non_trainable_variables, losses)`."
msgstr ""

#: keras.src.layers.layer.Layer.stateless_call:17 of
msgid ""
"Note: `non_trainable_variables` include not only non-trainable weights "
"such as `BatchNormalization` statistics, but also RNG seed state (if "
"there are any random operations part of the layer, such as dropout), and "
"`Metric` state (if there are any metrics attached to the layer). These "
"are all elements of state of the layer."
msgstr ""

#: keras.src.layers.layer.Layer.stateless_call:25 of
msgid ""
"```python model = ... data = ... trainable_variables = "
"model.trainable_variables non_trainable_variables = "
"model.non_trainable_variables # Call the model with zero side effects "
"outputs, non_trainable_variables = model.stateless_call("
msgstr ""

#: keras.src.layers.layer.Layer.stateless_call:32 of
msgid "trainable_variables, non_trainable_variables, data,"
msgstr ""

#: keras.src.layers.layer.Layer.stateless_call:35 of
msgid ""
") # Attach the updated state to the model # (until you do this, the model"
" is still in its pre-call state). for ref_var, value in zip("
msgstr ""

#: keras.src.layers.layer.Layer.stateless_call:39 of
msgid "model.non_trainable_variables, non_trainable_variables"
msgstr ""

#: keras.src.layers.layer.Layer.stateless_call:40 of
msgid "):"
msgstr ""

#: keras.src.layers.layer.Layer.stateless_call:41 of
msgid "ref_var.assign(value)"
msgstr ""

#: keras.src.models.model.Model.summary:1 of
msgid "Prints a string summary of the network."
msgstr ""

#: keras.src.models.model.Model.summary:3 of
msgid ""
"Total length of printed lines (e.g. set this to adapt the display to "
"different terminal window sizes)."
msgstr ""

#: keras.src.models.model.Model.summary:6 of
msgid ""
"Relative or absolute positions of log elements in each line. If not "
"provided, becomes `[0.3, 0.6, 0.70, 1.]`. Defaults to `None`."
msgstr ""

#: keras.src.models.model.Model.summary:9 of
msgid ""
"Print function to use. By default, prints to `stdout`. If `stdout` "
"doesn't work in your environment, change to `print`. It will be called on"
" each line of the summary. You can set it to a custom function in order "
"to capture the string summary."
msgstr ""

#: keras.src.models.model.Model.summary:14 of
msgid "Whether to expand the nested models. Defaults to `False`."
msgstr ""

#: keras.src.models.model.Model.summary:16 of
msgid "Whether to show if a layer is trainable. Defaults to `False`."
msgstr ""

#: keras.src.models.model.Model.summary:18 of
msgid ""
"a list or tuple of 2 strings, which is the starting layer name and ending"
" layer name (both inclusive) indicating the range of layers to be printed"
" in summary. It also accepts regex patterns instead of exact names. In "
"this case, the start predicate will be the first element that matches "
"`layer_range[0]` and the end predicate will be the last element that "
"matches `layer_range[1]`. By default `None` considers all layers of the "
"model."
msgstr ""

#: ../../source/api/applications/van.rst
#: ../../source/api/backends/backend_factory.rst
#: ../../source/api/backends/cupy_backend.rst
#: ../../source/api/backends/jax_backend.rst
#: ../../source/api/backends/numpy_backend.rst
#: ../../source/api/backends/pytorch_backend.rst
#: ../../source/api/backends/tensorflow_backend.rst
#: ../../source/api/basecircuit.rst ../../source/api/circuit.rst
#: ../../source/api/cons.rst ../../source/api/densitymatrix.rst
#: ../../source/api/gates.rst ../../source/api/keras.rst
#: ../../source/api/mps_base.rst ../../source/api/mpscircuit.rst
#: ../../source/api/quantum.rst ../../source/api/quditcircuit.rst
#: ../../source/api/quditgates.rst ../../source/api/stabilizercircuit.rst
#: ../../source/api/templates/lattice.rst ../../source/api/torchnn.rst
msgid "Raises"
msgstr ""

#: keras.src.models.model.Model.summary:28 of
msgid "if `summary()` is called before the model is built."
msgstr ""

#: of tensorcircuit.applications.van.MADE.supports_masking:1
#: tensorcircuit.applications.van.MaskedConv2D.supports_masking:1
#: tensorcircuit.applications.van.MaskedLinear.supports_masking:1
#: tensorcircuit.applications.van.NMF.supports_masking:1
#: tensorcircuit.applications.van.PixelCNN.supports_masking:1
#: tensorcircuit.applications.van.ResidualBlock.supports_masking:1
#: tensorcircuit.applications.vqes.Linear.supports_masking:1
#: tensorcircuit.keras.HardwareLayer.supports_masking:1
#: tensorcircuit.keras.QuantumLayer.supports_masking:1
msgid "Whether this layer supports computing a mask using `compute_mask`."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.test_on_batch:1 of
msgid "Test the model on a single batch of samples."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.test_on_batch:3
#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.train_on_batch:3 of
msgid "Input data. Must be array-like."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.test_on_batch:4
#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.train_on_batch:4 of
msgid "Target data. Must be array-like."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.test_on_batch:5
#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.train_on_batch:5 of
msgid ""
"Optional array of the same length as x, containing weights to apply to "
"the model's loss for each sample. In the case of temporal data, you can "
"pass a 2D array with shape `(samples, sequence_length)`, to apply a "
"different weight to every timestep of every sample."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.test_on_batch:14
#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.train_on_batch:22 of
msgid ""
"A scalar loss value (when no metrics and `return_dict=False`), a list of "
"loss and metric values (if there are metrics and `return_dict=False`), or"
" a dict of metric and loss values (if `return_dict=True`)."
msgstr ""

#: keras.src.models.model.Model.to_json:1 of
msgid "Returns a JSON string containing the network configuration."
msgstr ""

#: keras.src.models.model.Model.to_json:3 of
#, python-brace-format
msgid ""
"To load a network from a JSON save file, use "
"`keras.models.model_from_json(json_string, custom_objects={...})`."
msgstr ""

#: keras.src.models.model.Model.to_json:6 of
msgid "Additional keyword arguments to be passed to `json.dumps()`."
msgstr ""

#: keras.src.models.model.Model.to_json:9 of
msgid "A JSON string."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.train_on_batch:1 of
msgid "Runs a single gradient update on a single batch of data."
msgstr ""

#: keras.src.backend.tensorflow.trainer.TensorFlowTrainer.train_on_batch:10 of
msgid ""
"Optional dictionary mapping class indices (integers) to a weight (float) "
"to apply to the model's loss for the samples from this class during "
"training. This can be useful to tell the model to \"pay more attention\" "
"to samples from an under-represented class. When `class_weight` is "
"specified and targets have a rank of 2 or greater, either `y` must be "
"one-hot encoded, or an explicit final dimension of 1 must be included for"
" sparse class labels."
msgstr ""

#: of tensorcircuit.applications.van.MADE.trainable:1
#: tensorcircuit.applications.van.MaskedConv2D.trainable:1
#: tensorcircuit.applications.van.MaskedLinear.trainable:1
#: tensorcircuit.applications.van.NMF.trainable:1
#: tensorcircuit.applications.van.PixelCNN.trainable:1
#: tensorcircuit.applications.van.ResidualBlock.trainable:1
#: tensorcircuit.applications.vqes.Linear.trainable:1
#: tensorcircuit.keras.HardwareLayer.trainable:1
#: tensorcircuit.keras.QuantumLayer.trainable:1
msgid "Settable boolean, whether this layer should be trainable or not."
msgstr ""

#: of tensorcircuit.applications.van.MADE.trainable_variables:1
#: tensorcircuit.applications.van.MaskedConv2D.trainable_variables:1
#: tensorcircuit.applications.van.MaskedLinear.trainable_variables:1
#: tensorcircuit.applications.van.NMF.trainable_variables:1
#: tensorcircuit.applications.van.PixelCNN.trainable_variables:1
#: tensorcircuit.applications.van.ResidualBlock.trainable_variables:1
#: tensorcircuit.applications.vqes.Linear.trainable_variables:1
#: tensorcircuit.keras.HardwareLayer.trainable_variables:1
#: tensorcircuit.keras.QuantumLayer.trainable_variables:1
msgid "List of all trainable layer state."
msgstr ""

#: of tensorcircuit.applications.van.MADE.trainable_variables:3
#: tensorcircuit.applications.van.MaskedConv2D.trainable_variables:3
#: tensorcircuit.applications.van.MaskedLinear.trainable_variables:3
#: tensorcircuit.applications.van.NMF.trainable_variables:3
#: tensorcircuit.applications.van.PixelCNN.trainable_variables:3
#: tensorcircuit.applications.van.ResidualBlock.trainable_variables:3
#: tensorcircuit.applications.vqes.Linear.trainable_variables:3
#: tensorcircuit.keras.HardwareLayer.trainable_variables:3
#: tensorcircuit.keras.QuantumLayer.trainable_variables:3
msgid "This is equivalent to `layer.trainable_weights`."
msgstr ""

#: of tensorcircuit.applications.van.MADE.trainable_weights:1
#: tensorcircuit.applications.van.MaskedConv2D.trainable_weights:1
#: tensorcircuit.applications.van.MaskedLinear.trainable_weights:1
#: tensorcircuit.applications.van.NMF.trainable_weights:1
#: tensorcircuit.applications.van.PixelCNN.trainable_weights:1
#: tensorcircuit.applications.van.ResidualBlock.trainable_weights:1
#: tensorcircuit.applications.vqes.Linear.trainable_weights:1
#: tensorcircuit.keras.HardwareLayer.trainable_weights:1
#: tensorcircuit.keras.QuantumLayer.trainable_weights:1
msgid "List of all trainable weight variables of the layer."
msgstr ""

#: of tensorcircuit.applications.van.MADE.trainable_weights:3
#: tensorcircuit.applications.van.MaskedConv2D.trainable_weights:3
#: tensorcircuit.applications.van.MaskedLinear.trainable_weights:3
#: tensorcircuit.applications.van.NMF.trainable_weights:3
#: tensorcircuit.applications.van.PixelCNN.trainable_weights:3
#: tensorcircuit.applications.van.ResidualBlock.trainable_weights:3
#: tensorcircuit.applications.vqes.Linear.trainable_weights:3
#: tensorcircuit.keras.HardwareLayer.trainable_weights:3
#: tensorcircuit.keras.QuantumLayer.trainable_weights:3
msgid "These are the weights that get updated by the optimizer during training."
msgstr ""

#: of tensorcircuit.applications.van.MADE.variable_dtype:1
#: tensorcircuit.applications.van.MaskedConv2D.variable_dtype:1
#: tensorcircuit.applications.van.MaskedLinear.variable_dtype:1
#: tensorcircuit.applications.van.NMF.variable_dtype:1
#: tensorcircuit.applications.van.PixelCNN.variable_dtype:1
#: tensorcircuit.applications.van.ResidualBlock.variable_dtype:1
#: tensorcircuit.applications.vqes.Linear.variable_dtype:1
#: tensorcircuit.keras.HardwareLayer.variable_dtype:1
#: tensorcircuit.keras.QuantumLayer.variable_dtype:1
msgid "The dtype of the state (weights) of the layer."
msgstr ""

#: of tensorcircuit.applications.van.MADE.variables:1
#: tensorcircuit.applications.van.MaskedConv2D.variables:1
#: tensorcircuit.applications.van.MaskedLinear.variables:1
#: tensorcircuit.applications.van.NMF.variables:1
#: tensorcircuit.applications.van.PixelCNN.variables:1
#: tensorcircuit.applications.van.ResidualBlock.variables:1
#: tensorcircuit.applications.vqes.Linear.variables:1
#: tensorcircuit.keras.HardwareLayer.variables:1
#: tensorcircuit.keras.QuantumLayer.variables:1
msgid "List of all layer state, including random seeds."
msgstr ""

#: of tensorcircuit.applications.van.MADE.variables:3
#: tensorcircuit.applications.van.MaskedConv2D.variables:3
#: tensorcircuit.applications.van.MaskedLinear.variables:3
#: tensorcircuit.applications.van.NMF.variables:3
#: tensorcircuit.applications.van.PixelCNN.variables:3
#: tensorcircuit.applications.van.ResidualBlock.variables:3
#: tensorcircuit.applications.vqes.Linear.variables:3
#: tensorcircuit.keras.HardwareLayer.variables:3
#: tensorcircuit.keras.QuantumLayer.variables:3
msgid ""
"This extends `layer.weights` to include all state used by the layer "
"including `SeedGenerator`s."
msgstr ""

#: of tensorcircuit.applications.van.MADE.variables:6
#: tensorcircuit.applications.van.MaskedConv2D.variables:6
#: tensorcircuit.applications.van.MaskedLinear.variables:6
#: tensorcircuit.applications.van.NMF.variables:6
#: tensorcircuit.applications.van.PixelCNN.variables:6
#: tensorcircuit.applications.van.ResidualBlock.variables:6
#: tensorcircuit.applications.vqes.Linear.variables:6
#: tensorcircuit.keras.HardwareLayer.variables:6
#: tensorcircuit.keras.QuantumLayer.variables:6
msgid ""
"Note that metrics variables are not included here, use "
"`metrics_variables` to visit all the metric variables."
msgstr ""

#: of tensorcircuit.applications.van.MADE.weights:1
#: tensorcircuit.applications.van.MaskedConv2D.weights:1
#: tensorcircuit.applications.van.MaskedLinear.weights:1
#: tensorcircuit.applications.van.NMF.weights:1
#: tensorcircuit.applications.van.PixelCNN.weights:1
#: tensorcircuit.applications.van.ResidualBlock.weights:1
#: tensorcircuit.applications.vqes.Linear.weights:1
#: tensorcircuit.keras.HardwareLayer.weights:1
#: tensorcircuit.keras.QuantumLayer.weights:1
msgid "List of all weight variables of the layer."
msgstr ""

#: of tensorcircuit.applications.van.MADE.weights:3
#: tensorcircuit.applications.van.MaskedConv2D.weights:3
#: tensorcircuit.applications.van.MaskedLinear.weights:3
#: tensorcircuit.applications.van.NMF.weights:3
#: tensorcircuit.applications.van.PixelCNN.weights:3
#: tensorcircuit.applications.van.ResidualBlock.weights:3
#: tensorcircuit.applications.vqes.Linear.weights:3
#: tensorcircuit.keras.HardwareLayer.weights:3
#: tensorcircuit.keras.QuantumLayer.weights:3
msgid "Unlike, `layer.variables` this excludes metric state and random seeds."
msgstr ""

#: ../../source/api/applications/vqes.rst:2
msgid "tensorcircuit.applications.vqes"
msgstr ""

#: of tensorcircuit.applications.vqes:1
msgid "VQNHE application"
msgstr ""

#: keras.src.optimizers.schedules.learning_rate_schedule.LearningRateSchedule.from_config:1
#: of
msgid "Instantiates a `LearningRateSchedule` from its config."
msgstr ""

#: keras.src.optimizers.schedules.learning_rate_schedule.LearningRateSchedule.from_config:3
#: of
msgid "Output of `get_config()`."
msgstr ""

#: keras.src.optimizers.schedules.learning_rate_schedule.LearningRateSchedule.from_config:5
#: of
msgid "A `LearningRateSchedule` instance."
msgstr ""

#: of tensorcircuit.applications.vqes.Linear:1
msgid "Dense layer but with complex weights, used for building complex RBM"
msgstr ""

#: of tensorcircuit.applications.vqes.VQNHE.evaluation:1
msgid "VQNHE"
msgstr ""

#: of tensorcircuit.applications.vqes.VQNHE.evaluation:3
#: tensorcircuit.applications.vqes.VQNHE.evaluation:5
#: tensorcircuit.applications.vqes.VQNHE.plain_evaluation:3
#: tensorcircuit.applications.vqes.VQNHE.plain_evaluation:5
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.sort:3
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.sort:7
#: tensorcircuit.backends.cupy_backend.CuPyBackend.argmax:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.argmax:7
#: tensorcircuit.backends.cupy_backend.CuPyBackend.argmin:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.argmin:7
#: tensorcircuit.backends.cupy_backend.CuPyBackend.concat:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.cond:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.cond:5
#: tensorcircuit.backends.cupy_backend.CuPyBackend.cond:7
#: tensorcircuit.backends.cupy_backend.CuPyBackend.cond:9
#: tensorcircuit.backends.cupy_backend.CuPyBackend.coo_sparse_matrix:10
#: tensorcircuit.backends.cupy_backend.CuPyBackend.cumsum:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.cumsum:8
#: tensorcircuit.backends.cupy_backend.CuPyBackend.max:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.max:7
#: tensorcircuit.backends.cupy_backend.CuPyBackend.min:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.min:7
#: tensorcircuit.backends.cupy_backend.CuPyBackend.sigmoid:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.sigmoid:5
#: tensorcircuit.backends.cupy_backend.CuPyBackend.stop_gradient:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.stop_gradient:5
#: tensorcircuit.backends.cupy_backend.CuPyBackend.switch:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.switch:5
#: tensorcircuit.backends.cupy_backend.CuPyBackend.switch:7
#: tensorcircuit.backends.cupy_backend.CuPyBackend.tile:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.tile:7
#: tensorcircuit.backends.cupy_backend.CuPyBackend.unique_with_counts:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.vectorized_value_and_grad:29
#: tensorcircuit.backends.cupy_backend.CuPyBackend.vectorized_value_and_grad:36
#: tensorcircuit.backends.jax_backend.JaxBackend.argmax:3
#: tensorcircuit.backends.jax_backend.JaxBackend.argmax:7
#: tensorcircuit.backends.jax_backend.JaxBackend.argmin:3
#: tensorcircuit.backends.jax_backend.JaxBackend.argmin:7
#: tensorcircuit.backends.jax_backend.JaxBackend.concat:3
#: tensorcircuit.backends.jax_backend.JaxBackend.cond:3
#: tensorcircuit.backends.jax_backend.JaxBackend.cond:5
#: tensorcircuit.backends.jax_backend.JaxBackend.cond:7
#: tensorcircuit.backends.jax_backend.JaxBackend.cond:9
#: tensorcircuit.backends.jax_backend.JaxBackend.coo_sparse_matrix:10
#: tensorcircuit.backends.jax_backend.JaxBackend.cumsum:3
#: tensorcircuit.backends.jax_backend.JaxBackend.cumsum:8
#: tensorcircuit.backends.jax_backend.JaxBackend.max:3
#: tensorcircuit.backends.jax_backend.JaxBackend.max:7
#: tensorcircuit.backends.jax_backend.JaxBackend.min:3
#: tensorcircuit.backends.jax_backend.JaxBackend.min:7
#: tensorcircuit.backends.jax_backend.JaxBackend.sigmoid:3
#: tensorcircuit.backends.jax_backend.JaxBackend.sigmoid:5
#: tensorcircuit.backends.jax_backend.JaxBackend.sort:3
#: tensorcircuit.backends.jax_backend.JaxBackend.sort:7
#: tensorcircuit.backends.jax_backend.JaxBackend.stop_gradient:3
#: tensorcircuit.backends.jax_backend.JaxBackend.stop_gradient:5
#: tensorcircuit.backends.jax_backend.JaxBackend.switch:3
#: tensorcircuit.backends.jax_backend.JaxBackend.switch:5
#: tensorcircuit.backends.jax_backend.JaxBackend.switch:7
#: tensorcircuit.backends.jax_backend.JaxBackend.tile:3
#: tensorcircuit.backends.jax_backend.JaxBackend.tile:7
#: tensorcircuit.backends.jax_backend.JaxBackend.unique_with_counts:3
#: tensorcircuit.backends.jax_backend.JaxBackend.vectorized_value_and_grad:29
#: tensorcircuit.backends.jax_backend.JaxBackend.vectorized_value_and_grad:36
#: tensorcircuit.backends.numpy_backend.NumpyBackend.argmax:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.argmax:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.argmin:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.argmin:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.concat:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cond:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cond:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cond:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cond:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.coo_sparse_matrix:10
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cumsum:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cumsum:8
#: tensorcircuit.backends.numpy_backend.NumpyBackend.max:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.max:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.min:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.min:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sigmoid:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sigmoid:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sort:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sort:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stop_gradient:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stop_gradient:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.switch:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.switch:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.switch:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.tile:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.tile:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.unique_with_counts:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vectorized_value_and_grad:29
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vectorized_value_and_grad:36
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.argmax:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.argmax:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.argmin:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.argmin:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.concat:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cond:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cond:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cond:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cond:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.coo_sparse_matrix:10
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cumsum:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cumsum:8
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.max:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.max:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.min:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.min:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sigmoid:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sigmoid:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sort:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sort:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stop_gradient:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stop_gradient:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.switch:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.switch:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.switch:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tile:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tile:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.unique_with_counts:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vectorized_value_and_grad:29
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vectorized_value_and_grad:36
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.argmax:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.argmax:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.argmin:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.argmin:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.concat:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cond:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cond:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cond:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cond:9
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.coo_sparse_matrix:10
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cumsum:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cumsum:8
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.max:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.max:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.min:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.min:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.sigmoid:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.sigmoid:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.sort:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.sort:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stop_gradient:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stop_gradient:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.switch:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.switch:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.switch:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.tile:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.tile:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.unique_with_counts:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vectorized_value_and_grad:29
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vectorized_value_and_grad:36
#: tensorcircuit.basecircuit.BaseCircuit.sample_expectation_ps:53
#: tensorcircuit.cloud.abstraction.Device.list_properties:3
#: tensorcircuit.cloud.abstraction.Device.topology:3
#: tensorcircuit.cloud.abstraction.Device.topology_graph:5
#: tensorcircuit.cloud.tencent.submit_task:6
#: tensorcircuit.cloud.tencent.submit_task:8 tensorcircuit.quantum.count_d2s:10
#: tensorcircuit.quantum.count_d2s:14 tensorcircuit.quantum.count_s2d:4
#: tensorcircuit.quantum.count_s2d:10
#: tensorcircuit.simplify.pseudo_contract_between:3
#: tensorcircuit.simplify.pseudo_contract_between:5
#: tensorcircuit.simplify.pseudo_contract_between:7
#: tensorcircuit.templates.graphs.Line1D:3
#: tensorcircuit.templates.graphs.Line1D:7
msgid "[description]"
msgstr ""

#: of tensorcircuit.applications.vqes.VQNHE.plain_evaluation:1
msgid "VQE"
msgstr ""

#: ../../source/api/backends.rst:2
msgid "tensorcircuit.backends"
msgstr ""

#: ../../source/api/backends/backend_factory.rst:2
msgid "tensorcircuit.backends.backend_factory"
msgstr ""

#: of tensorcircuit.backends.backend_factory:1
msgid "Backend register"
msgstr ""

#: of tensorcircuit.backends.backend_factory.get_backend:1
msgid "Get the `tc.backend` object."
msgstr ""

#: of tensorcircuit.backends.backend_factory.get_backend:3
msgid "\"numpy\", \"tensorflow\", \"jax\", \"pytorch\""
msgstr ""

#: of tensorcircuit.backends.backend_factory.get_backend:5
msgid "Backend doesn't exist for `backend` argument."
msgstr ""

#: of tensorcircuit.backends.backend_factory.get_backend:6
#: tensorcircuit.cons.set_tensornetwork_backend:32
msgid "The `tc.backend` object that with all registered universal functions."
msgstr ""

#: ../../source/api/backends/cupy_backend.rst:2
msgid "tensorcircuit.backends.cupy_backend"
msgstr ""

#: of tensorcircuit.backends.cupy_backend:1
msgid "CuPy backend. Not in the tensornetwork package and highly experimental."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.abs:1
#: tensorcircuit.backends.jax_backend.JaxBackend.abs:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.abs:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.abs:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.abs:1
msgid ""
"Returns the elementwise absolute value of tensor. :param tensor: An input"
" tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.abs:4
#: tensorcircuit.backends.jax_backend.JaxBackend.abs:4
#: tensorcircuit.backends.numpy_backend.NumpyBackend.abs:4
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.abs:4
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.abs:4
msgid "Its elementwise absolute value."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.acos:1
#: tensorcircuit.backends.cupy_backend.CuPyBackend.asin:1
#: tensorcircuit.backends.jax_backend.JaxBackend.acos:1
#: tensorcircuit.backends.jax_backend.JaxBackend.asin:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.acos:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.asin:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.acos:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.asin:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.acos:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.asin:1
msgid "Return the acos of a tensor ``a``."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.sqrtmh:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.acos:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.acosh:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.asin:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.asinh:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.atan:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.atan2:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.atanh:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.copy:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.cosh:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.eigvalsh:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.kron:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.kron:5
#: tensorcircuit.backends.cupy_backend.CuPyBackend.numpy:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.sinh:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.tan:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.tanh:3
#: tensorcircuit.backends.jax_backend.JaxBackend.acos:3
#: tensorcircuit.backends.jax_backend.JaxBackend.acosh:3
#: tensorcircuit.backends.jax_backend.JaxBackend.asin:3
#: tensorcircuit.backends.jax_backend.JaxBackend.asinh:3
#: tensorcircuit.backends.jax_backend.JaxBackend.atan:3
#: tensorcircuit.backends.jax_backend.JaxBackend.atan2:3
#: tensorcircuit.backends.jax_backend.JaxBackend.atanh:3
#: tensorcircuit.backends.jax_backend.JaxBackend.copy:3
#: tensorcircuit.backends.jax_backend.JaxBackend.cosh:3
#: tensorcircuit.backends.jax_backend.JaxBackend.eigvalsh:3
#: tensorcircuit.backends.jax_backend.JaxBackend.kron:3
#: tensorcircuit.backends.jax_backend.JaxBackend.kron:5
#: tensorcircuit.backends.jax_backend.JaxBackend.numpy:3
#: tensorcircuit.backends.jax_backend.JaxBackend.sinh:3
#: tensorcircuit.backends.jax_backend.JaxBackend.tan:3
#: tensorcircuit.backends.jax_backend.JaxBackend.tanh:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.acos:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.acosh:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.asin:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.asinh:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.atan:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.atan2:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.atanh:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.copy:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cosh:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.eigvalsh:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.kron:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.kron:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.numpy:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sinh:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.tan:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.tanh:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.acos:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.acosh:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.asin:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.asinh:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.atan:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.atan2:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.atanh:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.copy:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cosh:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.eigvalsh:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.kron:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.kron:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.numpy:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sinh:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tan:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tanh:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.acos:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.acosh:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.asin:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.asinh:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.atan:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.atan2:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.atanh:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.copy:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cosh:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.eigvalsh:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.kron:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.kron:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.numpy:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.sinh:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.tan:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.tanh:3
msgid "tensor in matrix form"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.acos:5
#: tensorcircuit.backends.jax_backend.JaxBackend.acos:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.acos:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.acos:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.acos:5
msgid "acos of ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.acosh:1
#: tensorcircuit.backends.jax_backend.JaxBackend.acosh:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.acosh:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.acosh:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.acosh:1
msgid "Return the acosh of a tensor ``a``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.acosh:5
#: tensorcircuit.backends.jax_backend.JaxBackend.acosh:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.acosh:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.acosh:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.acosh:5
msgid "acosh of ``a``"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.addition:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.addition:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.addition:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.addition:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.addition:1
msgid ""
"Return the default addition of `tensor`. A backend can override such "
"implementation. :param tensor1: A tensor. :param tensor2: A tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.conj:4
#: tensorcircuit.backends.cupy_backend.CuPyBackend.cos:4
#: tensorcircuit.backends.cupy_backend.CuPyBackend.expm:4
#: tensorcircuit.backends.cupy_backend.CuPyBackend.multiply:7
#: tensorcircuit.backends.cupy_backend.CuPyBackend.sin:4
#: tensorcircuit.backends.cupy_backend.CuPyBackend.softmax:9
#: tensorcircuit.backends.jax_backend.JaxBackend.cos:4
#: tensorcircuit.backends.jax_backend.JaxBackend.expm:4
#: tensorcircuit.backends.jax_backend.JaxBackend.sin:4
#: tensorcircuit.backends.jax_backend.JaxBackend.softmax:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cos:4
#: tensorcircuit.backends.numpy_backend.NumpyBackend.expm:4
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sin:4
#: tensorcircuit.backends.numpy_backend.NumpyBackend.softmax:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cos:4
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.exp:4
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.expm:4
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sin:4
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.softmax:9
#: tensorcircuit.backends.pytorch_backend._conj_torch:4
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cos:4
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.expm:4
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.sin:4
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.softmax:9
#: tensorcircuit.shadows.entropy_shadow:4
#: tensorcircuit.shadows.expectation_ps_shadow:4
#: tensorcircuit.shadows.global_shadow_state:5
#: tensorcircuit.shadows.global_shadow_state1:6
#: tensorcircuit.shadows.global_shadow_state2:6
#: tensorcircuit.shadows.local_snapshot_states:4
#: tensorcircuit.shadows.local_snapshot_states:6
#: tensorcircuit.shadows.renyi_entropy_2:5
#: tensorcircuit.shadows.shadow_snapshots:4
#: tensorcircuit.shadows.shadow_snapshots:6 tensorcircuit.shadows.slice_sub:4
#: tensornetwork.backends.abstract_backend.AbstractBackend.addition:6
#: tensornetwork.backends.abstract_backend.AbstractBackend.divide:6
#: tensornetwork.backends.abstract_backend.AbstractBackend.exp:4
#: tensornetwork.backends.abstract_backend.AbstractBackend.log:4
#: tensornetwork.backends.abstract_backend.AbstractBackend.subtraction:6
#: tensornetwork.backends.jax.jax_backend.JaxBackend.addition:6
#: tensornetwork.backends.jax.jax_backend.JaxBackend.conj:4
#: tensornetwork.backends.jax.jax_backend.JaxBackend.divide:6
#: tensornetwork.backends.jax.jax_backend.JaxBackend.exp:4
#: tensornetwork.backends.jax.jax_backend.JaxBackend.log:4
#: tensornetwork.backends.jax.jax_backend.JaxBackend.multiply:7
#: tensornetwork.backends.jax.jax_backend.JaxBackend.subtraction:6
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.addition:6
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.conj:4
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.divide:6
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.exp:4
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.log:4
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.multiply:7
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.subtraction:6
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.addition:6
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.divide:6
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.multiply:7
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.subtraction:6
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.addition:6
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.conj:4
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.divide:6
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.exp:4
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.log:4
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.multiply:7
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.subtraction:6
msgid "Tensor"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.adjoint:1
msgid "Return the conjugate and transpose of a tensor ``a``"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.adjoint:3
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.det:3
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.expand_dims:5
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.ones_like:3
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.repeat:3
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.reshape2:3
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.reshaped:3
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.reshapem:3
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.top_k:3
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.zeros_like:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.all:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.any:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.relu:9
#: tensorcircuit.backends.jax_backend.JaxBackend.all:3
#: tensorcircuit.backends.jax_backend.JaxBackend.any:3
#: tensorcircuit.backends.jax_backend.JaxBackend.det:3
#: tensorcircuit.backends.jax_backend.JaxBackend.expand_dims:5
#: tensorcircuit.backends.jax_backend.JaxBackend.ones_like:3
#: tensorcircuit.backends.jax_backend.JaxBackend.relu:9
#: tensorcircuit.backends.jax_backend.JaxBackend.repeat:3
#: tensorcircuit.backends.jax_backend.JaxBackend.top_k:3
#: tensorcircuit.backends.jax_backend.JaxBackend.zeros_like:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.all:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.any:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.det:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.expand_dims:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.ones_like:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.relu:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.repeat:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.top_k:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.zeros_like:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.all:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.any:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.det:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.expand_dims:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.ones_like:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.relu:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.repeat:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.top_k:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.zeros_like:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.all:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.any:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.det:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.expand_dims:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.ones_like:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.relu:9
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.repeat:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.top_k:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.zeros_like:3
msgid "Input tensor"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.adjoint:5
msgid "adjoint tensor of ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.all:1
#: tensorcircuit.backends.jax_backend.JaxBackend.all:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.all:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.all:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.all:1
msgid "Test whether all array elements along a given axis evaluate to True."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.all:5
#: tensorcircuit.backends.jax_backend.JaxBackend.all:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.all:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.all:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.all:5
msgid ""
"Axis or axes along which a logical AND reduction is performed, defaults "
"to None"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.all:8
#: tensorcircuit.backends.jax_backend.JaxBackend.all:8
#: tensorcircuit.backends.numpy_backend.NumpyBackend.all:8
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.all:8
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.all:8
msgid "A new boolean or tensor resulting from the AND reduction"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.any:1
#: tensorcircuit.backends.jax_backend.JaxBackend.any:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.any:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.any:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.any:1
msgid "Check whether any element of the input tensor evaluates to True."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.any:5
#: tensorcircuit.backends.jax_backend.JaxBackend.any:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.any:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.any:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.any:5
msgid "True if any element is True, False otherwise"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.arange:1
#: tensorcircuit.backends.jax_backend.JaxBackend.arange:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.arange:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.arange:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.arange:1
msgid "Values are generated within the half-open interval [start, stop)"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.arange:3
#: tensorcircuit.backends.jax_backend.JaxBackend.arange:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.arange:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.arange:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.arange:3
msgid "start index"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.arange:5
#: tensorcircuit.backends.jax_backend.JaxBackend.arange:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.arange:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.arange:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.arange:5
msgid "end index, defaults to None"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.arange:7
#: tensorcircuit.backends.jax_backend.JaxBackend.arange:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.arange:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.arange:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.arange:7
msgid "steps, defaults to 1"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.argmax:1
#: tensorcircuit.backends.jax_backend.JaxBackend.argmax:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.argmax:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.argmax:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.argmax:1
msgid "Return the index of maximum of an array an axis."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.argmax:5
#: tensorcircuit.backends.cupy_backend.CuPyBackend.argmin:5
#: tensorcircuit.backends.jax_backend.JaxBackend.argmax:5
#: tensorcircuit.backends.jax_backend.JaxBackend.argmin:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.argmax:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.argmin:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.argmax:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.argmin:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.argmax:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.argmin:5
msgid "[description], defaults to 0, different behavior from numpy defaults!"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.argmin:1
#: tensorcircuit.backends.jax_backend.JaxBackend.argmin:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.argmin:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.argmin:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.argmin:1
msgid "Return the index of minimum of an array an axis."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.argsort:1
#: tensorcircuit.backends.jax_backend.JaxBackend.argsort:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.argsort:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.argsort:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.argsort:1
msgid "return the indices that would sort an array."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.argsort:3
#: tensorcircuit.backends.jax_backend.JaxBackend.argsort:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.argsort:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.argsort:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.argsort:3
msgid "the tensor to be sorted"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.argsort:5
#: tensorcircuit.backends.jax_backend.JaxBackend.argsort:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.argsort:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.argsort:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.argsort:5
msgid "the sorted axis, defaults to -1"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.argsort:7
#: tensorcircuit.backends.jax_backend.JaxBackend.argsort:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.argsort:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.argsort:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.argsort:7
msgid "the sorted indices"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.asin:5
#: tensorcircuit.backends.jax_backend.JaxBackend.asin:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.asin:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.asin:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.asin:5
msgid "asin of ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.asinh:1
#: tensorcircuit.backends.jax_backend.JaxBackend.asinh:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.asinh:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.asinh:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.asinh:1
msgid "Return the asinh of a tensor ``a``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.asinh:5
#: tensorcircuit.backends.jax_backend.JaxBackend.asinh:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.asinh:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.asinh:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.asinh:5
msgid "asinh of ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.atan:1
#: tensorcircuit.backends.jax_backend.JaxBackend.atan:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.atan:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.atan:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.atan:1
msgid "Return the atan of a tensor ``a``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.atan:5
#: tensorcircuit.backends.jax_backend.JaxBackend.atan:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.atan:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.atan:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.atan:5
msgid "atan of ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.atan2:1
#: tensorcircuit.backends.jax_backend.JaxBackend.atan2:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.atan2:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.atan2:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.atan2:1
msgid "Return the atan of a tensor ``y``/``x``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.atan2:5
#: tensorcircuit.backends.jax_backend.JaxBackend.atan2:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.atan2:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.atan2:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.atan2:5
msgid "atan2 of ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.atanh:1
#: tensorcircuit.backends.jax_backend.JaxBackend.atanh:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.atanh:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.atanh:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.atanh:1
msgid "Return the atanh of a tensor ``a``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.atanh:5
#: tensorcircuit.backends.jax_backend.JaxBackend.atanh:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.atanh:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.atanh:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.atanh:5
msgid "atanh of ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.bitwise_and:1
#: tensorcircuit.backends.jax_backend.JaxBackend.bitwise_and:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.bitwise_and:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.bitwise_and:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.bitwise_and:1
msgid "Compute the element-wise bitwise AND of two integer tensors."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.bitwise_and:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.bitwise_or:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.bitwise_xor:3
#: tensorcircuit.backends.jax_backend.JaxBackend.bitwise_and:3
#: tensorcircuit.backends.jax_backend.JaxBackend.bitwise_or:3
#: tensorcircuit.backends.jax_backend.JaxBackend.bitwise_xor:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.bitwise_and:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.bitwise_or:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.bitwise_xor:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.bitwise_and:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.bitwise_or:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.bitwise_xor:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.bitwise_and:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.bitwise_or:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.bitwise_xor:3
msgid "first input tensor"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.bitwise_and:5
#: tensorcircuit.backends.cupy_backend.CuPyBackend.bitwise_or:5
#: tensorcircuit.backends.cupy_backend.CuPyBackend.bitwise_xor:5
#: tensorcircuit.backends.jax_backend.JaxBackend.bitwise_and:5
#: tensorcircuit.backends.jax_backend.JaxBackend.bitwise_or:5
#: tensorcircuit.backends.jax_backend.JaxBackend.bitwise_xor:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.bitwise_and:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.bitwise_or:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.bitwise_xor:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.bitwise_and:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.bitwise_or:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.bitwise_xor:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.bitwise_and:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.bitwise_or:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.bitwise_xor:5
msgid "second input tensor"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.bitwise_and:7
#: tensorcircuit.backends.jax_backend.JaxBackend.bitwise_and:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.bitwise_and:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.bitwise_and:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.bitwise_and:7
msgid "element-wise bitwise AND of ``x`` and ``y``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.bitwise_or:1
#: tensorcircuit.backends.jax_backend.JaxBackend.bitwise_or:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.bitwise_or:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.bitwise_or:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.bitwise_or:1
msgid "Compute the element-wise bitwise OR of two integer tensors."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.bitwise_or:7
#: tensorcircuit.backends.jax_backend.JaxBackend.bitwise_or:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.bitwise_or:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.bitwise_or:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.bitwise_or:7
msgid "element-wise bitwise OR of ``x`` and ``y``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.bitwise_xor:1
#: tensorcircuit.backends.jax_backend.JaxBackend.bitwise_xor:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.bitwise_xor:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.bitwise_xor:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.bitwise_xor:1
msgid "Compute the element-wise bitwise XOR of two integer tensors."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.bitwise_xor:7
#: tensorcircuit.backends.jax_backend.JaxBackend.bitwise_xor:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.bitwise_xor:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.bitwise_xor:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.bitwise_xor:7
msgid "element-wise bitwise XOR of ``x`` and ``y``"
msgstr ""

#: of
#: tensornetwork.backends.abstract_backend.AbstractBackend.broadcast_left_multiplication:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.broadcast_left_multiplication:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.broadcast_left_multiplication:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.broadcast_left_multiplication:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.broadcast_left_multiplication:1
msgid ""
"Perform broadcasting for multiplication of `tensor1` onto `tensor2`, i.e."
" `tensor1` * tensor2`, where `tensor2` is an arbitrary tensor and "
"`tensor1` is a one-dimensional tensor. The broadcasting is applied to the"
" first index of `tensor2`. :param tensor1: A tensor. :param tensor2: A "
"tensor."
msgstr ""

#: of
#: tensornetwork.backends.abstract_backend.AbstractBackend.broadcast_left_multiplication:8
#: tensornetwork.backends.abstract_backend.AbstractBackend.broadcast_right_multiplication:8
#: tensornetwork.backends.jax.jax_backend.JaxBackend.broadcast_left_multiplication:8
#: tensornetwork.backends.jax.jax_backend.JaxBackend.broadcast_right_multiplication:8
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.broadcast_left_multiplication:8
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.broadcast_right_multiplication:8
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.broadcast_left_multiplication:8
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.broadcast_right_multiplication:8
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.broadcast_left_multiplication:8
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.broadcast_right_multiplication:8
msgid "The result of multiplying `tensor1` onto `tensor2`."
msgstr ""

#: of
#: tensornetwork.backends.abstract_backend.AbstractBackend.broadcast_right_multiplication:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.broadcast_right_multiplication:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.broadcast_right_multiplication:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.broadcast_right_multiplication:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.broadcast_right_multiplication:1
msgid ""
"Perform broadcasting for multiplication of `tensor2` onto `tensor1`, i.e."
" `tensor1` * tensor2`, where `tensor1` is an arbitrary tensor and "
"`tensor2` is a one-dimensional tensor. The broadcasting is applied to the"
" last index of `tensor1`. :param tensor1: A tensor. :param tensor2: A "
"tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.cast:1
#: tensorcircuit.backends.jax_backend.JaxBackend.cast:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cast:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cast:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cast:1
msgid "Cast the tensor dtype of a ``a``."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.sizen:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.cast:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.imag:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.real:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.size:3
#: tensorcircuit.backends.jax_backend.JaxBackend.cast:3
#: tensorcircuit.backends.jax_backend.JaxBackend.imag:3
#: tensorcircuit.backends.jax_backend.JaxBackend.real:3
#: tensorcircuit.backends.jax_backend.JaxBackend.size:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cast:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.imag:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.real:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.size:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cast:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.imag:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.real:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.size:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cast:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.imag:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.real:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.size:3
msgid "tensor"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.cast:5
#: tensorcircuit.backends.jax_backend.JaxBackend.cast:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cast:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cast:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cast:5
msgid "\"float32\", \"float64\", \"complex64\", \"complex128\""
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.cast:7
#: tensorcircuit.backends.jax_backend.JaxBackend.cast:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cast:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cast:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cast:7
msgid "``a`` of new dtype"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.clip:1
#: tensorcircuit.backends.jax_backend.JaxBackend.clip:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.clip:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.clip:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.clip:1
msgid ""
"Clip (limit) the values of a tensor element-wise to the range [a_min, "
"a_max]."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.clip:3
#: tensorcircuit.backends.jax_backend.JaxBackend.clip:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.clip:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.clip:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.clip:3
msgid ""
"Each element in the input tensor `a` is compared against the "
"corresponding bounds `a_min` and `a_max`. If a value in `a` is less than "
"`a_min`, it is set to `a_min`; if greater than `a_max`, it is set to "
"`a_max`. Otherwise, the value is left unchanged. The result preserves the"
" dtype and device of the input."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.clip:8
#: tensorcircuit.backends.jax_backend.JaxBackend.clip:8
#: tensorcircuit.backends.numpy_backend.NumpyBackend.clip:8
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.clip:8
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.clip:8
msgid "Input tensor containing values to be clipped."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.clip:10
#: tensorcircuit.backends.jax_backend.JaxBackend.clip:10
#: tensorcircuit.backends.numpy_backend.NumpyBackend.clip:10
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.clip:10
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.clip:10
msgid ""
"Lower bound (minimum value) for clipping. Can be a scalar tensor or "
"broadcastable to the shape of `a`."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.clip:13
#: tensorcircuit.backends.jax_backend.JaxBackend.clip:13
#: tensorcircuit.backends.numpy_backend.NumpyBackend.clip:13
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.clip:13
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.clip:13
msgid ""
"Upper bound (maximum value) for clipping. Can be a scalar tensor or "
"broadcastable to the shape of `a`."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.clip:16
#: tensorcircuit.backends.jax_backend.JaxBackend.clip:16
#: tensorcircuit.backends.numpy_backend.NumpyBackend.clip:16
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.clip:16
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.clip:16
msgid ""
"A tensor with the same shape as `a`, where all values are clipped to lie "
"within the interval [a_min, a_max]."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.clip:20
#: tensorcircuit.backends.jax_backend.JaxBackend.clip:20
#: tensorcircuit.backends.numpy_backend.NumpyBackend.clip:20
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.clip:20
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.clip:20
msgid "If the backend does not implement `clip`."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.concat:1
#: tensorcircuit.backends.jax_backend.JaxBackend.concat:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.concat:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.concat:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.concat:1
msgid "Join a sequence of arrays along an existing axis."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.concat:5
#: tensorcircuit.backends.cupy_backend.CuPyBackend.vectorized_value_and_grad:31
#: tensorcircuit.backends.jax_backend.JaxBackend.concat:5
#: tensorcircuit.backends.jax_backend.JaxBackend.vectorized_value_and_grad:31
#: tensorcircuit.backends.numpy_backend.NumpyBackend.concat:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vectorized_value_and_grad:31
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.concat:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vectorized_value_and_grad:31
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.concat:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vectorized_value_and_grad:31
msgid "[description], defaults to 0"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.cond:1
#: tensorcircuit.backends.jax_backend.JaxBackend.cond:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cond:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cond:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cond:1
msgid ""
"The native cond for XLA compiling, wrapper for ``tf.cond`` and limited "
"functionality of ``jax.lax.cond``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.conj:1
#: tensorcircuit.backends.pytorch_backend._conj_torch:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.conj:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.conj:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.conj:1
msgid "Return the complex conjugate of `tensor` :param tensor: A tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.convert_to_tensor:1
#: tensorcircuit.backends.jax_backend.JaxBackend.convert_to_tensor:1
#: tensorcircuit.backends.numpy_backend._convert_to_tensor_numpy:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.convert_to_tensor:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.convert_to_tensor:1
msgid "Convert a np.array or a tensor to a tensor type for the backend."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.coo_sparse_matrix:1
#: tensorcircuit.backends.jax_backend.JaxBackend.coo_sparse_matrix:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.coo_sparse_matrix:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.coo_sparse_matrix:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.coo_sparse_matrix:1
msgid ""
"Generate the coo format sparse matrix from indices and values, which is "
"the only sparse format supported in different ML backends."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.coo_sparse_matrix:4
#: tensorcircuit.backends.jax_backend.JaxBackend.coo_sparse_matrix:4
#: tensorcircuit.backends.numpy_backend.NumpyBackend.coo_sparse_matrix:4
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.coo_sparse_matrix:4
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.coo_sparse_matrix:4
msgid "shape [n, 2] for n non zero values in the returned matrix"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.coo_sparse_matrix:6
#: tensorcircuit.backends.jax_backend.JaxBackend.coo_sparse_matrix:6
#: tensorcircuit.backends.numpy_backend.NumpyBackend.coo_sparse_matrix:6
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.coo_sparse_matrix:6
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.coo_sparse_matrix:6
msgid "shape [n]"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.coo_sparse_matrix:8
#: tensorcircuit.backends.jax_backend.JaxBackend.coo_sparse_matrix:8
#: tensorcircuit.backends.numpy_backend.NumpyBackend.coo_sparse_matrix:8
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.coo_sparse_matrix:8
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.coo_sparse_matrix:8
msgid "Tuple[int, ...]"
msgstr ""

#: of
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.coo_sparse_matrix_from_numpy:1
msgid "Generate the coo format sparse matrix from scipy coo sparse matrix."
msgstr ""

#: of
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.coo_sparse_matrix_from_numpy:3
msgid "Scipy coo format sparse matrix"
msgstr ""

#: of
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.coo_sparse_matrix_from_numpy:5
msgid "SparseTensor in backend format"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.copy:1
#: tensorcircuit.backends.jax_backend.JaxBackend.copy:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.copy:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.copy:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.copy:1
msgid "Return the copy of ``a``, matrix exponential."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.copy:5
#: tensorcircuit.backends.jax_backend.JaxBackend.copy:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.copy:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.copy:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.copy:5
msgid "the copy tensor of ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.cos:1
#: tensorcircuit.backends.jax_backend.JaxBackend.cos:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cos:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cos:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cos:1
msgid "Return cos of `tensor`. :param tensor: A tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.cosh:1
#: tensorcircuit.backends.jax_backend.JaxBackend.cosh:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cosh:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cosh:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cosh:1
msgid "Return the cosh of a tensor ``a``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.cosh:5
#: tensorcircuit.backends.jax_backend.JaxBackend.cosh:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cosh:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cosh:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cosh:5
msgid "cosh of ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.cumsum:1
#: tensorcircuit.backends.jax_backend.JaxBackend.cumsum:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cumsum:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cumsum:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cumsum:1
msgid "Return the cumulative sum of the elements along a given axis."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.cumsum:5
#: tensorcircuit.backends.jax_backend.JaxBackend.cumsum:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.cumsum:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.cumsum:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.cumsum:5
msgid ""
"The default behavior is the same as numpy, different from tf/torch as "
"cumsum of the flatten 1D array, defaults to None"
msgstr ""

#: of
#: tensornetwork.backends.abstract_backend.AbstractBackend.deserialize_tensor:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.deserialize_tensor:1
msgid "Return a tensor given a serialized tensor string."
msgstr ""

#: of
#: tensornetwork.backends.abstract_backend.AbstractBackend.deserialize_tensor:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.deserialize_tensor:3
msgid "The input string representing a serialized tensor."
msgstr ""

#: of
#: tensornetwork.backends.abstract_backend.AbstractBackend.deserialize_tensor:5
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.deserialize_tensor:5
msgid "The tensor object represented by the string."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.det:1
#: tensorcircuit.backends.jax_backend.JaxBackend.det:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.det:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.det:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.det:1
msgid "Return the determinant scalar of a tensor ``a``"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.det:5
#: tensorcircuit.backends.jax_backend.JaxBackend.det:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.det:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.det:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.det:5
msgid "determinant of ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.device:1
#: tensorcircuit.backends.jax_backend.JaxBackend.device:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.device:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.device:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.device:1
msgid "get the universal device str for the tensor, in the format of tf"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.device:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.device_move:3
#: tensorcircuit.backends.jax_backend.JaxBackend.device:3
#: tensorcircuit.backends.jax_backend.JaxBackend.device_move:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.device:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.device_move:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.device:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.device_move:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.device:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.device_move:3
#: tensorcircuit.interfaces.tensortrans.which_backend:3
msgid "the tensor"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.device:5
#: tensorcircuit.backends.jax_backend.JaxBackend.device:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.device:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.device:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.device:5
msgid "device str where the tensor lives on"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.device_move:1
#: tensorcircuit.backends.jax_backend.JaxBackend.device_move:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.device_move:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.device_move:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.device_move:1
msgid "move tensor ``a`` to device ``dev``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.device_move:5
#: tensorcircuit.backends.jax_backend.JaxBackend.device_move:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.device_move:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.device_move:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.device_move:5
msgid "device str or device obj in corresponding backend"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.device_move:7
#: tensorcircuit.backends.jax_backend.JaxBackend.device_move:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.device_move:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.device_move:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.device_move:7
msgid "the tensor on new device"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.diagflat:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.diagflat:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.diagflat:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.diagflat:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.diagflat:1
msgid ""
"Flattens tensor and creates a new matrix of zeros with its elements on "
"the k'th diagonal. :param tensor: A tensor. :param k: The diagonal upon "
"which to place its elements."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.diagflat:6
#: tensornetwork.backends.jax.jax_backend.JaxBackend.diagflat:6
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.diagflat:6
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.diagflat:6
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.diagflat:6
msgid "A new tensor with all zeros save the specified diagonal."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.diagonal:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.diagonal:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.diagonal:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.diagonal:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.diagonal:1
msgid "Return specified diagonals."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.diagonal:3
#: tensornetwork.backends.jax.jax_backend.JaxBackend.diagonal:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.diagonal:3
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.diagonal:3
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.diagonal:3
msgid ""
"If tensor is 2-D, returns the diagonal of tensor with the given offset, "
"i.e., the collection of elements of the form a[i, i+offset]. If a has "
"more than two dimensions, then the axes specified by axis1 and axis2 are "
"used to determine the 2-D sub-array whose diagonal is returned. The shape"
" of the resulting array can be determined by removing axis1 and axis2 and"
" appending an index to the right equal to the size of the resulting "
"diagonals."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.diagonal:11
#: tensornetwork.backends.jax.jax_backend.JaxBackend.diagonal:11
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.diagonal:11
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.diagonal:11
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.diagonal:11
msgid ""
"This function only extracts diagonals. If you wish to create diagonal "
"matrices from vectors, use diagflat."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.reshape:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.shape_tuple:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.tensordot:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.shape_tuple:3
#: tensorcircuit.backends.tensorflow_backend._tensordot_tf:3
#: tensornetwork.backends.abstract_backend.AbstractBackend.diagonal:14
#: tensornetwork.backends.abstract_backend.AbstractBackend.shape_tensor:3
#: tensornetwork.backends.abstract_backend.AbstractBackend.slice:3
#: tensornetwork.backends.abstract_backend.AbstractBackend.trace:10
#: tensornetwork.backends.jax.jax_backend.JaxBackend.diagonal:14
#: tensornetwork.backends.jax.jax_backend.JaxBackend.reshape:3
#: tensornetwork.backends.jax.jax_backend.JaxBackend.shape_tensor:3
#: tensornetwork.backends.jax.jax_backend.JaxBackend.shape_tuple:3
#: tensornetwork.backends.jax.jax_backend.JaxBackend.slice:3
#: tensornetwork.backends.jax.jax_backend.JaxBackend.tensordot:3
#: tensornetwork.backends.jax.jax_backend.JaxBackend.trace:10
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.diagonal:14
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.reshape:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.shape_tensor:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.shape_tuple:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.slice:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.tensordot:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.trace:10
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.diagonal:14
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.reshape:3
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.shape_tensor:3
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.shape_tuple:3
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.slice:3
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.tensordot:3
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.trace:13
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.diagonal:14
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.reshape:3
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.shape_tensor:3
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.slice:3
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.trace:10
msgid "A tensor."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.diagonal:15
#: tensornetwork.backends.abstract_backend.AbstractBackend.trace:11
#: tensornetwork.backends.jax.jax_backend.JaxBackend.diagonal:15
#: tensornetwork.backends.jax.jax_backend.JaxBackend.trace:11
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.diagonal:15
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.trace:11
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.diagonal:15
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.diagonal:15
msgid "Offset of the diagonal from the main diagonal."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.diagonal:16
#: tensornetwork.backends.abstract_backend.AbstractBackend.diagonal:19
#: tensornetwork.backends.abstract_backend.AbstractBackend.trace:12
#: tensornetwork.backends.abstract_backend.AbstractBackend.trace:15
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.diagonal:16
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.diagonal:19
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.trace:12
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.trace:15
msgid ""
"Axis to be used as the first/second axis of the 2D sub-arrays from which "
"the diagonals should be taken. Defaults to second-last/last axis."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.diagonal:23
#: tensornetwork.backends.jax.jax_backend.JaxBackend.diagonal:23
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.diagonal:23
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.diagonal:25
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.diagonal:33
msgid ""
"A dim = min(1, tensor.ndim - 2) tensor storing                     the "
"batched diagonals."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.diagonal:25
#: tensornetwork.backends.jax.jax_backend.JaxBackend.diagonal:25
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.diagonal:25
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.diagonal:27
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.diagonal:35
msgid "A dim = min(1, tensor.ndim - 2) tensor storing"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.diagonal:26
#: tensornetwork.backends.jax.jax_backend.JaxBackend.diagonal:26
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.diagonal:26
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.diagonal:28
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.diagonal:36
msgid "the batched diagonals."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.divide:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.divide:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.divide:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.divide:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.divide:1
msgid ""
"Return the default divide of `tensor`. A backend can override such "
"implementation. :param tensor1: A tensor. :param tensor2: A tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.dtype:1
#: tensorcircuit.backends.jax_backend.JaxBackend.dtype:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.dtype:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.dtype:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.dtype:1
msgid "Obtain dtype string for tensor ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.dtype:3
#: tensorcircuit.backends.jax_backend.JaxBackend.dtype:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.dtype:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.dtype:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.dtype:3
msgid "The tensor"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.dtype:5
#: tensorcircuit.backends.jax_backend.JaxBackend.dtype:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.dtype:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.dtype:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.dtype:5
msgid "dtype str, such as \"complex64\""
msgstr ""

#: of tensorcircuit.backends.jax_backend._eigh_jax:1
#: tensorcircuit.backends.pytorch_backend._patched_eigh:1
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigh:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigh:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.eigh:1
msgid "Compute eigenvectors and eigenvalues of a hermitian matrix."
msgstr ""

#: of tensorcircuit.backends.jax_backend._eigh_jax:3
#: tensorcircuit.backends.pytorch_backend._patched_eigh:3
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigh:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigh:3
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.eigh:3
msgid "A symetric matrix."
msgstr ""

#: of tensorcircuit.backends.jax_backend._eigh_jax:5
#: tensorcircuit.backends.pytorch_backend._patched_eigh:5
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigh:5
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigh:5
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.eigh:5
msgid "The eigenvalues in ascending order. Tensor: The eigenvectors."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigs:1
msgid ""
"Arnoldi method for finding the lowest eigenvector-eigenvalue pairs of a "
"linear operator `A`. `A` is a callable implementing the matrix-vector "
"product. If no `initial_state` is provided then `shape` and `dtype` have "
"to be passed so that a suitable initial state can be randomly  generated."
" :param A: A (sparse) implementation of a linear operator :param arsg: A "
"list of arguments to `A`.  `A` will be called as"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigs:8
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh:8
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh:8
msgid "`res = A(initial_state, *args)`."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigs:9
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh:9
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigs:11
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh:9
msgid ""
"An initial vector for the algorithm. If `None`, a random initial `Tensor`"
" is created using the `numpy.random.randn` method."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigs:12
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh:12
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh_lanczos:10
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:44
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:44
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:42
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigs:14
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh:12
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh_lanczos:10
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eigsh_lanczos:10
msgid "The shape of the input-dimension of `A`."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigs:13
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh:13
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh_lanczos:11
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigs:15
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh:13
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh_lanczos:11
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eigsh_lanczos:11
msgid ""
"The dtype of the input `A`. If both no `initial_state` is provided, a "
"random initial state with shape `shape` and dtype `dtype` is created."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigs:15
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh:15
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh_lanczos:13
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:47
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:47
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:45
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigs:17
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh:15
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh_lanczos:13
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eigsh_lanczos:13
msgid "The number of iterations (number of krylov vectors)."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigs:16
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh:16
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh_lanczos:14
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigs:18
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh:16
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh_lanczos:14
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eigsh_lanczos:14
msgid ""
"The nummber of eigenvector-eigenvalue pairs to be computed. If `numeig > "
"1`, `reorthogonalize` has to be `True`."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigs:18
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh:18
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigs:20
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh:18
msgid "The desired precision of the eigenvalus. Uses"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigs:19
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh:19
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh:19
msgid ""
"['LM' | 'SM' | 'LR' | 'SR' | 'LI' | 'SI'] Which `k` eigenvectors and "
"eigenvalues to find:     'LM' : largest magnitude     'SM' : smallest "
"magnitude     'LR' : largest real part     'SR' : smallest real part     "
"'LI' : largest imaginary part     'SI' : smallest imaginary part Note "
"that not all of those might be supported by specialized backends."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigs:19
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh:19
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh:19
msgid ""
"['LM' | 'SM' | 'LR' | 'SR' | 'LI' | 'SI'] Which `k` eigenvectors and "
"eigenvalues to find:"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigs:21
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh:21
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh:21
msgid ""
"'LM' : largest magnitude 'SM' : smallest magnitude 'LR' : largest real "
"part 'SR' : smallest real part 'LI' : largest imaginary part 'SI' : "
"smallest imaginary part"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigs:27
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh:27
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh:27
msgid "Note that not all of those might be supported by specialized backends."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigs:28
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh:28
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigs:28
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh:28
msgid "The maximum number of iterations."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigs:30
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh:30
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigs:30
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh:30
msgid ""
"An array of `numeig` lowest eigenvalues `list`: A list of `numeig` lowest"
" eigenvectors"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigs:32
#: tensornetwork.backends.abstract_backend.AbstractBackend.eigsh:32
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh:32
#: tensornetwork.matrixproductstates.base_mps.BaseMPS.apply_transfer_operator:12
#: tensornetwork.matrixproductstates.base_mps.BaseMPS.get_tensor:10
#: tensornetwork.matrixproductstates.base_mps.BaseMPS.position:10
#: tensornetwork.matrixproductstates.finite_mps.FiniteMPS.canonicalize:9
msgid "`Tensor`"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigsh:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh:1
msgid ""
"Lanczos method for finding the lowest eigenvector-eigenvalue pairs of a "
"symmetric (hermitian) linear operator `A`. `A` is a callable implementing"
" the matrix-vector product. If no `initial_state` is provided then "
"`shape` and `dtype` have to be passed so that a suitable initial state "
"can be randomly  generated. :param A: A (sparse) implementation of a "
"linear operator :param arsg: A list of arguments to `A`.  `A` will be "
"called as"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigsh_lanczos:1
msgid ""
"Lanczos method for finding the lowest eigenvector-eigenvalue pairs of "
"`A`. :param A: A (sparse) implementation of a linear operator."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigsh_lanczos:4
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh_lanczos:4
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eigsh_lanczos:4
msgid ""
"Call signature of `A` is `res = A(vector, *args)`, where `vector` can be "
"an arbitrary `Tensor`, and `res.shape` has to be `vector.shape`."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigsh_lanczos:6
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:40
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:40
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:38
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigs:9
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh_lanczos:6
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eigsh_lanczos:6
msgid ""
"A list of arguments to `A`.  `A` will be called as `res = "
"A(initial_state, *args)`."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigsh_lanczos:8
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:40
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh_lanczos:8
msgid ""
"An initial vector for the Lanczos algorithm. If `None`, a random initial "
"`Tensor` is created using the `backend.randn` method"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigsh_lanczos:16
msgid ""
"The desired precision of the eigenvalus. Uses "
"`backend.norm(eigvalsnew[0:numeig] - eigvalsold[0:numeig]) < tol` as "
"stopping criterion between two diagonalization steps of the tridiagonal "
"operator."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigsh_lanczos:20
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:52
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh_lanczos:20
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eigsh_lanczos:20
msgid ""
"Stopping criterion for Lanczos iteration. If a Krylov vector :math: `x_n`"
" has an L2 norm :math:`\\lVert x_n\\rVert < delta`, the iteration is "
"stopped. It means that an (approximate) invariant subspace has been "
"found."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigsh_lanczos:25
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eigsh_lanczos:25
msgid ""
"The tridiagonal Operator is diagonalized every `ndiag` iterations to "
"check convergence."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigsh_lanczos:27
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:60
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eigsh_lanczos:27
msgid ""
"If `True`, Krylov vectors are kept orthogonal by explicit "
"orthogonalization (more costly than `reorthogonalize=False`)"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigsh_lanczos:30
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh_lanczos:28
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eigsh_lanczos:30
msgid ""
"(eigvals, eigvecs)  eigvals: A list of `numeig` lowest eigenvalues  "
"eigvecs: A list of `numeig` lowest eigenvectors"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigsh_lanczos:32
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:61
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:61
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:65
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh_lanczos:30
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eigsh_lanczos:32
msgid "(eigvals, eigvecs)"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eigsh_lanczos:33
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh_lanczos:31
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eigsh_lanczos:33
msgid ""
"eigvals: A list of `numeig` lowest eigenvalues eigvecs: A list of "
"`numeig` lowest eigenvectors"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.eigvalsh:1
#: tensorcircuit.backends.jax_backend.JaxBackend.eigvalsh:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.eigvalsh:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.eigvalsh:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.eigvalsh:1
msgid "Get the eigenvalues of matrix ``a``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.eigvalsh:5
#: tensorcircuit.backends.jax_backend.JaxBackend.eigvalsh:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.eigvalsh:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.eigvalsh:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.eigvalsh:5
msgid "eigenvalues of ``a``"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.einsum:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.einsum:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.einsum:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.einsum:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.einsum:1
msgid "Calculate sum of products of tensors according to expression."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eps:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eps:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eps:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eps:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.eps:1
msgid "Return machine epsilon for given `dtype`"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eps:3
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eps:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eps:3
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eps:3
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.eps:3
msgid "A dtype."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.eps:5
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eps:5
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eps:5
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eps:5
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.eps:5
msgid "Machine epsilon."
msgstr ""

#: of tensorcircuit.backends.pytorch_backend.PyTorchBackend.exp:1
#: tensornetwork.backends.abstract_backend.AbstractBackend.exp:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.exp:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.exp:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.exp:1
msgid "Return elementwise exp of `tensor`. :param tensor: A tensor."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.expand_dims:1
#: tensorcircuit.backends.jax_backend.JaxBackend.expand_dims:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.expand_dims:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.expand_dims:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.expand_dims:1
msgid ""
"Expand the shape of a tensor. Insert a new axis that will appear at the "
"`axis` position in the expanded tensor shape."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.expand_dims:7
#: tensorcircuit.backends.jax_backend.JaxBackend.expand_dims:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.expand_dims:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.expand_dims:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.expand_dims:7
msgid "Position in the expanded axes where the new axis is placed"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.expand_dims:9
#: tensorcircuit.backends.jax_backend.JaxBackend.expand_dims:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.expand_dims:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.expand_dims:9
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.expand_dims:9
msgid "Output tensor with the number of dimensions increased by one."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.expm:1
#: tensorcircuit.backends.jax_backend.JaxBackend.expm:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.expm:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.expm:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.expm:1
msgid "Return expm log of `matrix`, matrix exponential. :param matrix: A tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.eye:1
#: tensorcircuit.backends.jax_backend.JaxBackend.eye:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.eye:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.eye:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.eye:1
msgid "Return an identity matrix of dimension `dim`"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.eye:2
#: tensorcircuit.backends.jax_backend.JaxBackend.eye:2
#: tensorcircuit.backends.numpy_backend.NumpyBackend.eye:2
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.eye:2
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.eye:2
msgid ""
"Depending on specific backends, `dim` has to be either an int (numpy, "
"torch, tensorflow) or a `ShapeType` object (for block-sparse backends). "
"Block-sparse behavior is currently not supported"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.eye:6
#: tensorcircuit.backends.cupy_backend.CuPyBackend.eye:9
#: tensorcircuit.backends.jax_backend.JaxBackend.eye:6
#: tensorcircuit.backends.jax_backend.JaxBackend.eye:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.eye:6
#: tensorcircuit.backends.numpy_backend.NumpyBackend.eye:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.eye:6
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.eye:9
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.eye:6
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.eye:9
msgid "The dimension of the returned matrix."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.eye:8
#: tensorcircuit.backends.jax_backend.JaxBackend.eye:8
#: tensorcircuit.backends.numpy_backend.NumpyBackend.eye:8
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.eye:8
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.eye:8
msgid "The dtype of the returned matrix."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.floor:1
#: tensorcircuit.backends.jax_backend.JaxBackend.floor:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.floor:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.floor:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.floor:1
msgid "Compute the element-wise floor of the input tensor."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.floor:3
#: tensorcircuit.backends.jax_backend.JaxBackend.floor:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.floor:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.floor:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.floor:3
msgid ""
"This operation returns a new tensor with the largest integers less than "
"or equal to each element of the input tensor, i.e. it rounds each value "
"down towards negative infinity."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.floor:7
#: tensorcircuit.backends.jax_backend.JaxBackend.floor:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.floor:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.floor:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.floor:7
msgid "Input tensor containing numeric values."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.floor:9
#: tensorcircuit.backends.jax_backend.JaxBackend.floor:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.floor:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.floor:9
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.floor:9
msgid ""
"A tensor with the same shape as `a`, where each element is the floored "
"value of the corresponding element in `a`."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.floor:13
#: tensorcircuit.backends.jax_backend.JaxBackend.floor:13
#: tensorcircuit.backends.numpy_backend.NumpyBackend.floor:13
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.floor:13
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.floor:13
msgid "If the backend does not provide an implementation for `floor`."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.floor_divide:1
#: tensorcircuit.backends.jax_backend.JaxBackend.floor_divide:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.floor_divide:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.floor_divide:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.floor_divide:1
msgid "Compute the element-wise floor division of two tensors."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.floor_divide:3
#: tensorcircuit.backends.jax_backend.JaxBackend.floor_divide:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.floor_divide:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.floor_divide:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.floor_divide:3
msgid ""
"This operation returns a new tensor containing the result of dividing `x`"
" by `y` and rounding each element down towards negative infinity. The "
"semantics are equivalent to the Python `//` operator:"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.floor_divide:8
#: tensorcircuit.backends.jax_backend.JaxBackend.floor_divide:8
#: tensorcircuit.backends.numpy_backend.NumpyBackend.floor_divide:8
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.floor_divide:8
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.floor_divide:8
msgid "result[i] = floor(x[i] / y[i])"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.floor_divide:10
#: tensorcircuit.backends.jax_backend.JaxBackend.floor_divide:10
#: tensorcircuit.backends.numpy_backend.NumpyBackend.floor_divide:10
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.floor_divide:10
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.floor_divide:10
msgid "Broadcasting is supported according to the backend's rules."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.floor_divide:12
#: tensorcircuit.backends.jax_backend.JaxBackend.floor_divide:12
#: tensorcircuit.backends.numpy_backend.NumpyBackend.floor_divide:12
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.floor_divide:12
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.floor_divide:12
msgid "Dividend tensor."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.floor_divide:14
#: tensorcircuit.backends.jax_backend.JaxBackend.floor_divide:14
#: tensorcircuit.backends.numpy_backend.NumpyBackend.floor_divide:14
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.floor_divide:14
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.floor_divide:14
msgid "Divisor tensor, must be broadcastable with `x`."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.floor_divide:16
#: tensorcircuit.backends.jax_backend.JaxBackend.floor_divide:16
#: tensorcircuit.backends.numpy_backend.NumpyBackend.floor_divide:16
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.floor_divide:16
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.floor_divide:16
msgid ""
"A tensor with the broadcasted shape of `x` and `y`, where each element is"
" the floored result of the division."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.floor_divide:20
#: tensorcircuit.backends.jax_backend.JaxBackend.floor_divide:20
#: tensorcircuit.backends.numpy_backend.NumpyBackend.floor_divide:20
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.floor_divide:20
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.floor_divide:20
msgid "If the backend does not provide an implementation for `floor_divide`."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.from_dlpack:1
#: tensorcircuit.backends.jax_backend.JaxBackend.from_dlpack:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.from_dlpack:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.from_dlpack:1
msgid "Transform a dlpack capsule to a tensor"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.from_dlpack:3
#: tensorcircuit.backends.jax_backend.JaxBackend.from_dlpack:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.from_dlpack:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.from_dlpack:3
msgid "the dlpack capsule"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.gather1d:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.gather1d:1
msgid ""
"Return ``operand[indices]``, both ``operand`` and ``indices`` are rank-1 "
"tensor."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.gather1d:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.gather1d:3
msgid "rank-1 tensor"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.gather1d:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.gather1d:5
msgid "rank-1 tensor with int dtype"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.gather1d:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.gather1d:7
msgid "``operand[indices]``"
msgstr ""

#: of
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.get_random_state:1
msgid "Get the backend specific random state object."
msgstr ""

#: of
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.get_random_state:3
msgid "The random seed for the generator, defaults to None"
msgstr ""

#: of
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.get_random_state:5
msgid "the backend specific random state object"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:1
msgid ""
"GMRES solves the linear system A @ x = b for x given a vector `b` and a "
"general (not necessarily symmetric/Hermitian) linear operator `A`."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:4
msgid ""
"As a Krylov method, GMRES does not require a concrete matrix "
"representation of the n by n `A`, but only a function `vector1 = "
"A_mv(vector0, *A_args, **A_kwargs)` prescribing a one-to-one linear map "
"from vector0 to vector1 (that is, A must be square, and thus vector0 and "
"vector1 the same size). If `A` is a dense matrix, or if it is a "
"symmetric/Hermitian operator, a different linear solver will usually be "
"preferable."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:12
msgid ""
"GMRES works by first constructing the Krylov basis K = (x0, A_mv@x0, "
"A_mv@A_mv@x0, ..., (A_mv^num_krylov_vectors)@x_0) and then solving a "
"certain dense linear system K @ q0 = q1 from whose solution x can be "
"approximated. For `num_krylov_vectors = n` the solution is provably exact"
" in infinite precision, but the expense is cubic in `num_krylov_vectors` "
"so one is typically interested in the `num_krylov_vectors << n` case. The"
" solution can in this case be repeatedly improved, to a point, by "
"restarting the Arnoldi iterations each time `num_krylov_vectors` is "
"reached. Unfortunately the optimal parameter choices balancing expense "
"and accuracy are difficult to predict in advance, so applying this "
"function requires a degree of experimentation."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:24
msgid ""
"In a tensor network code one is typically interested in A_mv implementing"
" some tensor contraction. This implementation thus allows `b` and `x0` to"
" be of whatever arbitrary, though identical, shape `b = A_mv(x0, ...)` "
"expects. Reshaping to and from a matrix problem is handled internally."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:29
msgid ""
"A function `v0 = A_mv(v, *A_args, **A_kwargs)` where `v0` and `v` have "
"the same shape."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:31
msgid "The `b` in `A @ x = b`; it should be of the shape `A_mv` operates on."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:33
msgid ""
"Positional arguments to `A_mv`, supplied to this interface as a list. "
"Default: None."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:36
msgid ""
"Keyword arguments to `A_mv`, supplied to this interface as a dictionary. "
"Default: None."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:39
msgid ""
"An optional guess solution. Zeros are used by default. If `x0` is "
"supplied, its shape and dtype must match those of `b`, or an error will "
"be thrown. Default: zeros."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:44
#: tensornetwork.backends.abstract_backend.AbstractBackend.gmres:48
msgid ""
"Solution tolerance to achieve, norm(residual) <= max(tol*norm(b), atol). "
"Default: tol=1E-05          atol=tol"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:44
#: tensornetwork.backends.abstract_backend.AbstractBackend.gmres:48
msgid ""
"Solution tolerance to achieve, norm(residual) <= max(tol*norm(b), atol). "
"Default: tol=1E-05"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:47
#: tensornetwork.backends.abstract_backend.AbstractBackend.gmres:51
msgid "atol=tol"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:52
msgid ""
": Size of the Krylov space to build at each restart.   Expense is cubic "
"in this parameter. It must be positive.   If greater than b.size, it will"
" be set to b.size.   Default: 20"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:53
msgid ": Size of the Krylov space to build at each restart."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:54
msgid ""
"Expense is cubic in this parameter. It must be positive. If greater than "
"b.size, it will be set to b.size. Default: 20"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:57
msgid ""
"The Krylov space will be repeatedly rebuilt up to this many times. Large "
"values of this argument should be used only with caution, since "
"especially for nearly symmetric matrices and small `num_krylov_vectors` "
"convergence might well freeze at a value significantly larger than `tol`."
" Default: 1."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:63
msgid ""
"Inverse of the preconditioner of A; see the docstring for "
"`scipy.sparse.linalg.gmres`. This is only supported in the numpy backend."
" Supplying this argument to other backends will trigger "
"NotImplementedError. Default: None."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:69
msgid ""
"-if `x0` is supplied but its shape differs from that of `b`.     -in "
"NumPy, if the ARPACK solver reports a breakdown (which      usually "
"indicates some kind of floating point issue).     -if num_krylov_vectors "
"is 0 or exceeds b.size.     -if tol was negative.     -if M was supplied "
"with any backend but NumPy."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.gmres:71
msgid ""
"The converged solution. It has the same shape as `b`. info    : 0 if "
"convergence was achieved, the number of restarts otherwise."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.grad:1
#: tensorcircuit.backends.jax_backend.JaxBackend.grad:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.grad:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.grad:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.grad:1
msgid "Return the function which is the grad function of input ``f``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.grad:13
#: tensorcircuit.backends.cupy_backend.CuPyBackend.value_and_grad:13
#: tensorcircuit.backends.jax_backend.JaxBackend.grad:13
#: tensorcircuit.backends.jax_backend.JaxBackend.value_and_grad:13
#: tensorcircuit.backends.numpy_backend.NumpyBackend.grad:13
#: tensorcircuit.backends.numpy_backend.NumpyBackend.value_and_grad:13
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.grad:13
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.value_and_grad:13
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.grad:13
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.value_and_grad:13
msgid "the function to be differentiated"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.grad:15
#: tensorcircuit.backends.cupy_backend.CuPyBackend.value_and_grad:15
#: tensorcircuit.backends.jax_backend.JaxBackend.grad:15
#: tensorcircuit.backends.jax_backend.JaxBackend.value_and_grad:15
#: tensorcircuit.backends.numpy_backend.NumpyBackend.grad:15
#: tensorcircuit.backends.numpy_backend.NumpyBackend.value_and_grad:15
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.grad:15
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.value_and_grad:15
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.grad:15
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.value_and_grad:15
msgid ""
"the position of args in ``f`` that are to be differentiated, defaults to "
"be 0"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.grad:17
#: tensorcircuit.backends.jax_backend.JaxBackend.grad:17
#: tensorcircuit.backends.numpy_backend.NumpyBackend.grad:17
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.grad:17
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.grad:17
msgid "the grad function of ``f`` with the same set of arguments as ``f``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.i:1
#: tensorcircuit.backends.jax_backend.JaxBackend.i:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.i:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.i:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.i:1
msgid "Return 1.j in as a tensor compatible with the backend."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.i:3
#: tensorcircuit.backends.jax_backend.JaxBackend.i:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.i:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.i:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.i:3
msgid "\"complex64\" or \"complex128\""
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.i:5
#: tensorcircuit.backends.jax_backend.JaxBackend.i:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.i:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.i:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.i:5
msgid "1.j tensor"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.imag:1
#: tensorcircuit.backends.jax_backend.JaxBackend.imag:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.imag:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.imag:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.imag:1
msgid "Return the elementwise imaginary value of a tensor ``a``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.imag:5
#: tensorcircuit.backends.jax_backend.JaxBackend.imag:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.imag:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.imag:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.imag:5
msgid "imaginary value of ``a``"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randc:1
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randc:1
msgid "Categorical random sampler with internally managed random state."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randc:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randc:5
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randc:3
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randc:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randc:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randc:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randc:5
msgid "The possible options"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randc:5
#: tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randc:7
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randc:5
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randc:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randc:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randc:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randc:7
msgid "Sampling output shape"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randc:7
#: tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randc:9
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randc:7
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randc:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randc:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randc:9
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randc:9
msgid ""
"probability for each option in a, defaults to None, as equal probability "
"distribution"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randc:9
#: tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randc:11
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randc:9
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randc:11
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randc:11
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randc:11
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randc:11
msgid "Samples from the categorical distribution"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randn:1
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randn:1
msgid ""
"Call the random normal function with the random state management behind "
"the scene."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randn:3
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randu:3
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randn:3
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randu:3
msgid "The shape of the output tensor, defaults to 1"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randn:5
#: tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randn:9
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randn:5
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randn:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randn:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randn:9
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randn:9
msgid "The mean of the normal distribution, defaults to 0"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randn:7
#: tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randn:11
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randn:7
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randn:11
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randn:11
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randn:11
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randn:11
msgid "The standard deviation of the normal distribution, defaults to 1"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randn:9
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randu:9
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randn:9
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randu:9
msgid ""
"The real data type of the output tensor, defaults to None (global "
"rdtypestr)"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randn:11
#: tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randn:15
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randn:11
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randn:15
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randn:15
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randn:15
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randn:15
msgid "Samples from the normal distribution"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randu:1
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randu:1
msgid ""
"Call the uniform random function with the random state management behind "
"the scene."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randu:5
#: tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randu:7
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randu:5
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randu:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randu:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randu:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randu:7
msgid "The lower bound of the uniform distribution, defaults to 0"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randu:7
#: tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randu:9
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randu:7
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randu:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randu:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randu:9
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randu:9
msgid "The upper bound of the uniform distribution, defaults to 1"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.implicit_randu:11
#: tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randu:13
#: tensorcircuit.backends.jax_backend.JaxBackend.implicit_randu:11
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randu:13
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randu:13
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randu:13
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randu:13
msgid "Samples from the uniform distribution"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.index_update:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.index_update:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.index_update:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.index_update:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.index_update:1
msgid "Update `tensor` at elements defined by `mask` with value `assignee`."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.index_update:3
#: tensornetwork.backends.jax.jax_backend.JaxBackend.index_update:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.index_update:3
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.index_update:3
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.index_update:3
msgid "A `Tensor` object."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.index_update:4
#: tensornetwork.backends.jax.jax_backend.JaxBackend.index_update:4
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.index_update:4
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.index_update:4
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.index_update:4
msgid "A boolean mask."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.index_update:5
#: tensornetwork.backends.jax.jax_backend.JaxBackend.index_update:5
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.index_update:5
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.index_update:5
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.index_update:5
msgid ""
"A scalar `Tensor`. The values to assigned to `tensor` at positions where "
"`mask` is `True`."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.inv:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.inv:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.inv:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.inv:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.inv:1
msgid "Compute the matrix inverse of `matrix`."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.inv:3
#: tensornetwork.backends.jax.jax_backend.JaxBackend.inv:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.inv:3
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.inv:3
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.inv:3
msgid "A matrix."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.inv:5
#: tensornetwork.backends.jax.jax_backend.JaxBackend.inv:5
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.inv:5
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.inv:5
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.inv:5
msgid "The inverse of `matrix`"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.is_sparse:1
#: tensorcircuit.backends.jax_backend.JaxBackend.is_sparse:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.is_sparse:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.is_sparse:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.is_sparse:1
msgid "Determine whether the type of input ``a`` is  ``sparse``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.is_sparse:3
#: tensorcircuit.backends.jax_backend.JaxBackend.is_sparse:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.is_sparse:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.is_sparse:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.is_sparse:3
msgid "input matrix ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.is_sparse:5
#: tensorcircuit.backends.jax_backend.JaxBackend.is_sparse:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.is_sparse:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.is_sparse:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.is_sparse:5
msgid "a bool indicating whether the matrix ``a`` is sparse"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.is_tensor:1
#: tensorcircuit.backends.jax_backend.JaxBackend.is_tensor:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.is_tensor:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.is_tensor:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.is_tensor:1
msgid "Return a boolean on whether ``a`` is a tensor in backend package."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.is_tensor:3
#: tensorcircuit.backends.jax_backend.JaxBackend.is_tensor:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.is_tensor:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.is_tensor:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.is_tensor:3
msgid "a tensor to be determined"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.is_tensor:5
#: tensorcircuit.backends.jax_backend.JaxBackend.is_tensor:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.is_tensor:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.is_tensor:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.is_tensor:5
msgid "whether ``a`` is a tensor"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.item:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.item:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.item:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.item:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.item:1
msgid "Return the item of a 1-element tensor."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.item:3
#: tensornetwork.backends.jax.jax_backend.JaxBackend.item:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.item:3
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.item:3
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.item:3
msgid "A 1-element tensor"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.item:5
#: tensornetwork.backends.jax.jax_backend.JaxBackend.item:5
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.item:5
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.item:5
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.item:5
msgid "The value in tensor."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.jacrev:1
msgid "Compute the Jacobian of ``f`` using reverse mode AD."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.jacrev:3
msgid "The function whose Jacobian is required"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.jacfwd:5
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.jacrev:5
msgid "the position of the arg as Jacobian input, defaults to 0"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.jacrev:7
msgid "outer tuple for output, inner tuple for input args"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.jacfwd:1
msgid "Compute the Jacobian of ``f`` using the forward mode AD."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.jacfwd:3
msgid "the function whose Jacobian is required"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.jacfwd:7
msgid "outer tuple for input args, inner tuple for outputs"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.jaxy_scan:1
#: tensorcircuit.backends.jax_backend.JaxBackend.jaxy_scan:1
msgid "This API follows jax scan style. TF use plain for loop"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.jit:1
#: tensorcircuit.backends.jax_backend.JaxBackend.jit:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.jit:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.jit:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.jit:1
msgid ""
"Return a jitted or graph-compiled version of `fun` for JAX backend. For "
"all other backends returns `fun`. :param fun: Callable :param args: "
"Arguments to `fun`. :param kwargs: Keyword arguments to `fun`."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.jit:7
#: tensorcircuit.backends.jax_backend.JaxBackend.jit:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.jit:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.jit:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.jit:7
msgid "jitted/graph-compiled version of `fun`, or just `fun`."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.jvp:1
#: tensorcircuit.backends.jax_backend.JaxBackend.jvp:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.jvp:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.jvp:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.jvp:1
msgid ""
"Function that computes a (forward-mode) Jacobian-vector product of ``f``."
" Strictly speaking, this function is value_and_jvp."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.jvp:4
#: tensorcircuit.backends.jax_backend.JaxBackend.jvp:4
#: tensorcircuit.backends.numpy_backend.NumpyBackend.jvp:4
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.jvp:4
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.jvp:4
msgid "The function to compute jvp"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.jvp:6
#: tensorcircuit.backends.cupy_backend.CuPyBackend.vjp:7
#: tensorcircuit.backends.jax_backend.JaxBackend.jvp:6
#: tensorcircuit.backends.jax_backend.JaxBackend.vjp:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.jvp:6
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vjp:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.jvp:6
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vjp:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.jvp:6
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vjp:7
msgid "input for ``f``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.jvp:8
#: tensorcircuit.backends.jax_backend.JaxBackend.jvp:8
#: tensorcircuit.backends.numpy_backend.NumpyBackend.jvp:8
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.jvp:8
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.jvp:8
msgid "tangents"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.jvp:10
#: tensorcircuit.backends.jax_backend.JaxBackend.jvp:10
#: tensorcircuit.backends.numpy_backend.NumpyBackend.jvp:10
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.jvp:10
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.jvp:10
msgid ""
"(``f(*inputs)``, jvp_tensor), where jvp_tensor is the same shape as the "
"output of ``f``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.kron:1
#: tensorcircuit.backends.jax_backend.JaxBackend.kron:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.kron:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.kron:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.kron:1
msgid "Return the kronecker product of two matrices ``a`` and ``b``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.kron:7
#: tensorcircuit.backends.jax_backend.JaxBackend.kron:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.kron:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.kron:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.kron:7
msgid "kronecker product of ``a`` and ``b``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.left_shift:1
#: tensorcircuit.backends.jax_backend.JaxBackend.left_shift:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.left_shift:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.left_shift:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.left_shift:1
msgid "Shift the bits of an integer x to the left y bits."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.left_shift:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.mod:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.right_shift:3
#: tensorcircuit.backends.jax_backend.JaxBackend.left_shift:3
#: tensorcircuit.backends.jax_backend.JaxBackend.mod:3
#: tensorcircuit.backends.jax_backend.JaxBackend.right_shift:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.left_shift:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.mod:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.right_shift:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.left_shift:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.mod:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.right_shift:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.left_shift:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.mod:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.right_shift:3
msgid "input values"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.left_shift:5
#: tensorcircuit.backends.cupy_backend.CuPyBackend.right_shift:5
#: tensorcircuit.backends.jax_backend.JaxBackend.left_shift:5
#: tensorcircuit.backends.jax_backend.JaxBackend.right_shift:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.left_shift:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.right_shift:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.left_shift:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.right_shift:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.left_shift:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.right_shift:5
msgid "Number of bits shift to ``x``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.left_shift:7
#: tensorcircuit.backends.cupy_backend.CuPyBackend.right_shift:7
#: tensorcircuit.backends.jax_backend.JaxBackend.left_shift:7
#: tensorcircuit.backends.jax_backend.JaxBackend.right_shift:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.left_shift:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.right_shift:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.left_shift:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.right_shift:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.left_shift:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.right_shift:7
msgid "result with the same shape as ``x``"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.lexsort:1
#: tensorcircuit.backends.jax_backend.JaxBackend.lexsort:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.lexsort:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.lexsort:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.lexsort:1
msgid "Perform an indirect sort using a sequence of keys."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.lexsort:3
#: tensorcircuit.backends.jax_backend.JaxBackend.lexsort:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.lexsort:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.lexsort:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.lexsort:3
msgid "Sequence of tensors of the same shape"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.lexsort:5
#: tensorcircuit.backends.jax_backend.JaxBackend.lexsort:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.lexsort:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.lexsort:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.lexsort:5
msgid "Axis to sort along, defaults to -1"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.lexsort:7
#: tensorcircuit.backends.jax_backend.JaxBackend.lexsort:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.lexsort:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.lexsort:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.lexsort:7
msgid "Indices that would sort the keys"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.lobpcg:1
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.lobpcg_standard:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.lobpcg_standard:1
msgid ""
"Compute top-k eigenpairs via LOBPCG for Hermitian matrices. k is "
"determined by the number of columns in x0. By default, this method finds "
"the largest algebraic eigenvalues."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.lobpcg:5
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.lobpcg_standard:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.lobpcg_standard:5
msgid "linear operator or matrix tensor"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.lobpcg:7
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.lobpcg_standard:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.lobpcg_standard:7
msgid "initial guess matrix of shape (n, k)"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.lobpcg:9
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.lobpcg_standard:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.lobpcg_standard:9
msgid "maximum iterations"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.lobpcg:11
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.lobpcg_standard:11
#: tensorcircuit.backends.numpy_backend.NumpyBackend.lobpcg_standard:11
msgid "convergence tolerance"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.lobpcg:13
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.lobpcg_standard:13
#: tensorcircuit.backends.numpy_backend.NumpyBackend.lobpcg_standard:13
msgid "(eigenvalues, eigenvectors, iterations)"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.log:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.log:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.log:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.log:1
msgid "Return elementwise natural logarithm of `tensor`. :param tensor: A tensor."
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend._matmul_tf:1
#: tensornetwork.backends.abstract_backend.AbstractBackend.matmul:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.matmul:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.matmul:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.matmul:1
msgid ""
"Perform a possibly batched matrix-matrix multiplication between `tensor1`"
" and `tensor2`. The following behaviour is similar to `numpy.matmul`: - "
"If both arguments are 2-D they are multiplied like conventional"
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend._matmul_tf:5
#: tensornetwork.backends.abstract_backend.AbstractBackend.matmul:5
#: tensornetwork.backends.jax.jax_backend.JaxBackend.matmul:5
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.matmul:5
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.matmul:5
msgid "matrices."
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend._matmul_tf:6
#: tensornetwork.backends.abstract_backend.AbstractBackend.matmul:6
#: tensornetwork.backends.jax.jax_backend.JaxBackend.matmul:6
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.matmul:6
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.matmul:6
msgid ""
"If either argument is N-D, N > 2, it is treated as a stack of matrices "
"residing in the last two indexes and broadcast accordingly."
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend._matmul_tf:8
#: tensornetwork.backends.abstract_backend.AbstractBackend.matmul:8
#: tensornetwork.backends.jax.jax_backend.JaxBackend.matmul:8
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.matmul:8
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.matmul:8
msgid ""
"Both arguments to `matmul` have to be tensors of order >= 2. :param "
"tensor1: An input tensor. :param tensor2: An input tensor."
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend._matmul_tf:12
#: tensornetwork.backends.abstract_backend.AbstractBackend.matmul:12
#: tensornetwork.backends.jax.jax_backend.JaxBackend.matmul:12
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.matmul:12
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.matmul:12
msgid "The result of performing the matmul."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.max:1
#: tensorcircuit.backends.jax_backend.JaxBackend.max:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.max:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.max:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.max:1
msgid "Return the maximum of an array or maximum along an axis."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.max:5
#: tensorcircuit.backends.cupy_backend.CuPyBackend.min:5
#: tensorcircuit.backends.jax_backend.JaxBackend.max:5
#: tensorcircuit.backends.jax_backend.JaxBackend.min:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.max:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.min:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.max:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.min:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.max:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.min:5
#: tensorcircuit.keras.QuantumLayer.__init__:11
#: tensorcircuit.quantum.count_s2d:8
msgid "[description], defaults to None"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.mean:1
#: tensorcircuit.backends.jax_backend.JaxBackend.mean:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.mean:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.mean:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.mean:1
msgid "Compute the arithmetic mean for ``a`` along the specified ``axis``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.mean:3
#: tensorcircuit.backends.jax_backend.JaxBackend.mean:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.mean:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.mean:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.mean:3
msgid "tensor to take average"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.mean:5
#: tensorcircuit.backends.jax_backend.JaxBackend.mean:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.mean:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.mean:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.mean:5
msgid "the axis to take mean, defaults to None indicating sum over flatten array"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.mean:7
#: tensorcircuit.backends.jax_backend.JaxBackend.mean:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.mean:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.mean:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.mean:7
#: tensorcircuit.interfaces.torch.torch_interface_kws:26
msgid "_description_, defaults to False"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.meshgrid:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.meshgrid:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.meshgrid:1
msgid "Return coordinate matrices from coordinate vectors."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.meshgrid:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.meshgrid:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.meshgrid:3
msgid "coordinate vectors"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.meshgrid:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.meshgrid:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.meshgrid:5
msgid ""
"keyword arguments for meshgrid, typically includes 'indexing' which can "
"be 'ij' (matrix indexing) or 'xy' (Cartesian indexing). - 'ij': matrix "
"indexing, first dimension corresponds to rows (default) - 'xy': Cartesian"
" indexing, first dimension corresponds to columns Example:    >>> x, y = "
"backend.meshgrid([0, 1], [0, 2], indexing='xy')    Shapes:    - x.shape "
"== (2, 2)  # rows correspond to y vector length    - y.shape == (2, 2)"
"    Values:    x = [[0, 1],        [0, 1]]    y = [[0, 0],        [2, 2]]"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.meshgrid:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.meshgrid:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.meshgrid:5
msgid ""
"keyword arguments for meshgrid, typically includes 'indexing' which can "
"be 'ij' (matrix indexing) or 'xy' (Cartesian indexing). - 'ij': matrix "
"indexing, first dimension corresponds to rows (default) - 'xy': Cartesian"
" indexing, first dimension corresponds to columns Example:"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.meshgrid:20
#: tensorcircuit.backends.numpy_backend.NumpyBackend.meshgrid:20
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.meshgrid:20
msgid "list of coordinate matrices"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.min:1
#: tensorcircuit.backends.jax_backend.JaxBackend.min:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.min:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.min:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.min:1
msgid "Return the minimum of an array or minimum along an axis."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.mod:1
#: tensorcircuit.backends.jax_backend.JaxBackend.mod:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.mod:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.mod:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.mod:1
msgid ""
"Compute y-mod of x (negative number behavior is not guaranteed to be "
"consistent)"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.mod:5
#: tensorcircuit.backends.jax_backend.JaxBackend.mod:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.mod:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.mod:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.mod:5
msgid "mod ``y``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.mod:7
#: tensorcircuit.backends.jax_backend.JaxBackend.mod:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.mod:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.mod:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.mod:7
msgid "results"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.multiply:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.multiply:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.multiply:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.multiply:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.multiply:1
msgid "Return the default multiplication of `tensor`."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.multiply:3
#: tensornetwork.backends.jax.jax_backend.JaxBackend.multiply:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.multiply:3
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.multiply:3
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.multiply:3
msgid ""
"A backend can override such implementation. :param tensor1: A tensor. "
":param tensor2: A tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.norm:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.norm:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.norm:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.norm:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.norm:1
msgid "Calculate the L2-norm of the elements of `tensor`"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.numpy:1
#: tensorcircuit.backends.jax_backend.JaxBackend.numpy:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.numpy:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.numpy:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.numpy:1
msgid ""
"Return the numpy array of a tensor ``a``, but may not work in a jitted "
"function."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.numpy:5
#: tensorcircuit.backends.jax_backend.JaxBackend.numpy:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.numpy:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.numpy:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.numpy:5
msgid "numpy array of ``a``"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.one_hot:1
msgid "See doc for :py:meth:`onehot`"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.onehot:1
#: tensorcircuit.backends.jax_backend.JaxBackend.onehot:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.onehot:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.onehot:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.onehot:1
msgid ""
"One-hot encodes the given ``a``. Each index in the input ``a`` is encoded"
" as a vector of zeros of length ``num`` with the element at index set to "
"one:"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.onehot:5
#: tensorcircuit.backends.jax_backend.JaxBackend.onehot:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.onehot:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.onehot:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.onehot:5
msgid "input tensor"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.onehot:7
#: tensorcircuit.backends.jax_backend.JaxBackend.onehot:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.onehot:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.onehot:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.onehot:7
msgid "number of features in onehot dimension"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.onehot:9
#: tensorcircuit.backends.jax_backend.JaxBackend.onehot:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.onehot:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.onehot:9
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.onehot:9
msgid "onehot tensor with the last extra dimension"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.ones:1
#: tensorcircuit.backends.jax_backend.JaxBackend.ones:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.ones:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.ones:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.ones:1
msgid ""
"Return an ones-matrix of dimension `dim` Depending on specific backends, "
"`dim` has to be either an int (numpy, torch, tensorflow) or a `ShapeType`"
" object (for block-sparse backends). Block-sparse behavior is currently "
"not supported :param shape: The dimension of the returned matrix. :type "
"shape: int :param dtype: The dtype of the returned matrix."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.ones_like:1
#: tensorcircuit.backends.jax_backend.JaxBackend.ones_like:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.ones_like:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.ones_like:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.ones_like:1
msgid "Return a tensor of ones with the same shape as a."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.ones_like:5
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.zeros_like:5
#: tensorcircuit.backends.jax_backend.JaxBackend.ones_like:5
#: tensorcircuit.backends.jax_backend.JaxBackend.zeros_like:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.ones_like:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.zeros_like:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.ones_like:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.zeros_like:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.ones_like:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.zeros_like:5
msgid "Optional dtype of the output tensor"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.ones_like:7
#: tensorcircuit.backends.jax_backend.JaxBackend.ones_like:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.ones_like:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.ones_like:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.ones_like:7
msgid "A tensor of ones with the same shape as a"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.outer_product:1
#: tensorcircuit.backends.tensorflow_backend._outer_product_tf:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.outer_product:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.outer_product:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.outer_product:1
msgid "Calculate the outer product of the two given tensors."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.pivot:1
msgid ""
"Reshapes a tensor into a matrix, whose columns (rows) are the vectorized "
"dimensions to the left (right) of pivot_axis."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.pivot:4
msgid ""
"In other words, with tensor.shape = (1, 2, 4, 5) and pivot_axis=2, this "
"function returns an (8, 5) matrix."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.pivot:7
msgid "The tensor to pivot."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.pivot:8
msgid "The axis about which to pivot."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.pivot:10
msgid "The pivoted tensor."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.popc:1
#: tensorcircuit.backends.jax_backend.JaxBackend.popc:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.popc:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.popc:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.popc:1
msgid "Return the number of 1-bits in the binary representation of input ``a``."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.popc:3
#: tensorcircuit.backends.jax_backend.JaxBackend.popc:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.popc:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.popc:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.popc:3
msgid "Input tensor, integer type"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.popc:5
#: tensorcircuit.backends.jax_backend.JaxBackend.popc:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.popc:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.popc:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.popc:5
msgid "Element-wise popcount of ``a``"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.power:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.power:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.power:1
msgid "Returns the exponentiation of tensor a raised to b."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.power:2
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.power:2
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.power:2
msgid "If b is a tensor, then the exponentiation is element-wise"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.power:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.power:3
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.power:3
msgid ""
"between the two tensors, with a as the base and b as the power. Note that"
" a and b must be broadcastable to the same shape if b is a tensor."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.power:6
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.power:6
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.power:6
msgid "If b is a scalar, then the exponentiation is each value in a"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.power:7
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.power:7
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.power:7
msgid "raised to the power of b."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.power:9
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.power:9
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.power:9
msgid "The tensor containing the bases."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.power:10
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.power:10
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.power:10
msgid "The tensor containing the powers; or a single scalar as the power."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.power:12
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.power:12
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.power:12
msgid ""
"The tensor that is each element of a raised to the   power of b.  Note "
"that the shape of the returned tensor   is that produced by the broadcast"
" of a and b."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.power:14
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.power:14
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.power:14
msgid "The tensor that is each element of a raised to the"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.power:15
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.power:15
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.power:15
msgid ""
"power of b.  Note that the shape of the returned tensor is that produced "
"by the broadcast of a and b."
msgstr ""

#: of
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.probability_sample:1
msgid ""
"Drawn ``shots`` samples from probability distribution p, given the "
"external randomness determined by uniform distributed ``status`` tensor "
"or backend random generator ``g``. This method is similar with "
"``stateful_randc``, but it supports ``status`` beyond ``g``, which is "
"convenient when jit or vmap"
msgstr ""

#: of
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.probability_sample:6
msgid "Number of samples to draw with replacement"
msgstr ""

#: of
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.probability_sample:8
msgid "prbability vector"
msgstr ""

#: of
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.probability_sample:10
msgid ""
"external randomness as a tensor with each element drawn uniformly from "
"[0, 1], defaults to None"
msgstr ""

#: of
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.probability_sample:13
msgid "backend random genrator, defaults to None"
msgstr ""

#: of
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.probability_sample:15
msgid "The drawn sample as an int tensor"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.prod:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.prod:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.prod:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.prod:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.prod:1
msgid "product of tensors"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.qr:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.qr:1
msgid "Computes the QR decomposition of a tensor."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.randn:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.randn:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.randn:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.randn:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.randn:1
msgid ""
"Return a random-normal-matrix of dimension `dim` Depending on specific "
"backends, `dim` has to be either an int (numpy, torch, tensorflow) or a "
"`ShapeType` object (for block-sparse backends)."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.randn:5
#: tensornetwork.backends.jax.jax_backend.JaxBackend.randn:5
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.randn:5
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.randn:5
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.randn:5
msgid ""
"Block-sparse behavior is currently not supported :param shape: The "
"dimension of the returned matrix. :type shape: int :param dtype: The "
"dtype of the returned matrix. :param seed: The seed for the random number"
" generator"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.random_split:1
#: tensorcircuit.backends.jax_backend.JaxBackend.random_split:1
msgid ""
"A jax like split API, but it doesn't split the key generator for other "
"backends. It is just for a consistent interface of random code; make sure"
" you know what the function actually does. This function is mainly a "
"utility to write backend agnostic code instead of doing magic things."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.random_split:6
#: tensorcircuit.backends.jax_backend.JaxBackend.random_split:6
msgid "The random state/key to be split"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.random_split:8
#: tensorcircuit.backends.jax_backend.JaxBackend.random_split:8
msgid "A pair of random states/keys"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.random_uniform:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.random_uniform:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.random_uniform:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.random_uniform:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.random_uniform:1
msgid "Return a random uniform matrix of dimension `dim`."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.random_uniform:3
#: tensornetwork.backends.jax.jax_backend.JaxBackend.random_uniform:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.random_uniform:3
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.random_uniform:3
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.random_uniform:3
msgid ""
"Depending on specific backends, `dim` has to be either an int (numpy, "
"torch, tensorflow) or a `ShapeType` object (for block-sparse backends). "
"Block-sparse behavior is currently not supported :param shape: The "
"dimension of the returned matrix. :type shape: int :param boundaries: The"
" boundaries of the uniform distribution. :type boundaries: tuple :param "
"dtype: The dtype of the returned matrix. :param seed: The seed for the "
"random number generator"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.random_uniform:14
#: tensornetwork.backends.jax.jax_backend.JaxBackend.random_uniform:14
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.random_uniform:14
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.random_uniform:14
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.random_uniform:14
msgid "random uniform initialized tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.real:1
#: tensorcircuit.backends.jax_backend.JaxBackend.real:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.real:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.real:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.real:1
msgid "Return the elementwise real value of a tensor ``a``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.real:5
#: tensorcircuit.backends.jax_backend.JaxBackend.real:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.real:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.real:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.real:5
msgid "real value of ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.relu:1
#: tensorcircuit.backends.jax_backend.JaxBackend.relu:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.relu:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.relu:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.relu:1
msgid ""
"Rectified linear unit activation function. Computes the element-wise "
"function:"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.relu:4
#: tensorcircuit.backends.jax_backend.JaxBackend.relu:4
#: tensorcircuit.backends.numpy_backend.NumpyBackend.relu:4
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.relu:4
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.relu:4
#, python-brace-format
msgid "\\mathrm{relu}(x)=\\max(x,0)"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.relu:11
#: tensorcircuit.backends.jax_backend.JaxBackend.relu:11
#: tensorcircuit.backends.numpy_backend.NumpyBackend.relu:11
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.relu:11
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.relu:11
msgid "Tensor after relu"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.repeat:1
#: tensorcircuit.backends.jax_backend.JaxBackend.repeat:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.repeat:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.repeat:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.repeat:1
msgid "Repeat elements of an array."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.repeat:5
#: tensorcircuit.backends.jax_backend.JaxBackend.repeat:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.repeat:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.repeat:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.repeat:5
msgid "The number of repetitions for each element."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.repeat:7
#: tensorcircuit.backends.jax_backend.JaxBackend.repeat:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.repeat:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.repeat:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.repeat:7
msgid "The axis along which to repeat values."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.repeat:9
#: tensorcircuit.backends.jax_backend.JaxBackend.repeat:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.repeat:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.repeat:9
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.repeat:9
msgid "Output tensor which has the same shape as a, except along the given axis."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.reshape:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.reshape:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.reshape:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.reshape:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.reshape:1
msgid "Reshape tensor to the given shape."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.reshape:5
#: tensornetwork.backends.jax.jax_backend.JaxBackend.reshape:5
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.reshape:5
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.reshape:5
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.reshape:5
msgid "The reshaped tensor."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.reshape2:1
msgid "Reshape a tensor to the [2, 2, ...] shape."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.reshape2:5
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.reshaped:7
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.reshapem:5
msgid "the reshaped tensor"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.reshaped:1
msgid "Reshape a tensor to the [d, d, ...] shape."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.reshaped:5
msgid "edge length for each dimension"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.reshapem:1
msgid "Reshape a tensor to the [l, l] shape."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.reverse:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.reverse:1
msgid "return ``a[::-1]``, only 1D tensor is guaranteed for consistent behavior"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.reverse:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.reverse:3
msgid "1D tensor"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.reverse:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.reverse:5
msgid "1D tensor in reverse order"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.right_shift:1
#: tensorcircuit.backends.jax_backend.JaxBackend.right_shift:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.right_shift:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.right_shift:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.right_shift:1
msgid "Shift the bits of an integer x to the right y bits."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.rq:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.rq:1
msgid "Computes the RQ (reversed QR) decomposition of a tensor."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.scan:1
#: tensorcircuit.backends.jax_backend.JaxBackend.scan:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.scan:1
msgid "This API follows ``tf.scan`` covention, i.e. no ys supported as jax"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.scatter:1
#: tensorcircuit.backends.jax_backend.JaxBackend.scatter:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.scatter:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.scatter:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.scatter:1
msgid ""
"Equivalent to ``operand[indices] = updates`` with mode=\"update\". Also "
"supports mode=\"add\" and mode=\"sub\". Roughly equivalent to "
"``tf.tensor_scatter_nd_update``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.scatter:5
#: tensorcircuit.backends.jax_backend.JaxBackend.scatter:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.scatter:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.scatter:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.scatter:5
msgid "The tensor to be updated."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.scatter:7
#: tensorcircuit.backends.jax_backend.JaxBackend.scatter:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.scatter:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.scatter:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.scatter:7
msgid ""
"The indices to update. Expected shape: (batch_dims..., index_depth), "
"where index_depth <= rank(operand)."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.scatter:10
#: tensorcircuit.backends.jax_backend.JaxBackend.scatter:10
#: tensorcircuit.backends.numpy_backend.NumpyBackend.scatter:10
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.scatter:10
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.scatter:10
msgid ""
"The updates to apply. Expected shape: (batch_dims...,) + "
"operand.shape[index_depth:]."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.scatter:13
#: tensorcircuit.backends.jax_backend.JaxBackend.scatter:13
#: tensorcircuit.backends.numpy_backend.NumpyBackend.scatter:13
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.scatter:13
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.scatter:13
msgid "The update mode, \"update\", \"add\" or \"sub\", defaults to \"update\"."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.scatter:15
#: tensorcircuit.backends.jax_backend.JaxBackend.scatter:15
#: tensorcircuit.backends.numpy_backend.NumpyBackend.scatter:15
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.scatter:15
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.scatter:15
msgid "The updated tensor."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.schur:1
#: tensorcircuit.backends.jax_backend.JaxBackend.schur:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.schur:1
msgid "Compute Schur decomposition of a matrix."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.schur:5
#: tensorcircuit.backends.jax_backend.JaxBackend.schur:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.schur:5
msgid "_description_, defaults to \"real\""
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.schur:7
#: tensorcircuit.backends.jax_backend.JaxBackend.schur:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.schur:7
msgid "T, Z so that ZTZ^H = a"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.searchsorted:1
#: tensorcircuit.backends.jax_backend.JaxBackend.searchsorted:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.searchsorted:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.searchsorted:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.searchsorted:1
msgid "Find indices where elements should be inserted to maintain order."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.searchsorted:3
#: tensorcircuit.backends.jax_backend.JaxBackend.searchsorted:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.searchsorted:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.searchsorted:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.searchsorted:3
msgid "input array sorted in ascending order"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.searchsorted:5
#: tensorcircuit.backends.jax_backend.JaxBackend.searchsorted:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.searchsorted:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.searchsorted:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.searchsorted:5
msgid "value to inserted"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.searchsorted:7
#: tensorcircuit.backends.jax_backend.JaxBackend.searchsorted:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.searchsorted:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.searchsorted:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.searchsorted:7
msgid ""
"If `left`, the index of the first suitable location found is given. If "
"`right`, return the last such index. If there is no suitable index, "
"return either 0 or N (where N is the length of a), defaults to \"left\""
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.searchsorted:12
#: tensorcircuit.backends.jax_backend.JaxBackend.searchsorted:12
#: tensorcircuit.backends.numpy_backend.NumpyBackend.searchsorted:12
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.searchsorted:12
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.searchsorted:12
msgid ""
"Array of insertion points with the same shape as v, or an integer if v is"
" a scalar."
msgstr ""

#: of
#: tensornetwork.backends.abstract_backend.AbstractBackend.serialize_tensor:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.serialize_tensor:1
msgid "Return a string that serializes the given tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.sign:4
#: tensornetwork.backends.abstract_backend.AbstractBackend.serialize_tensor:3
#: tensornetwork.backends.jax.jax_backend.JaxBackend.sign:7
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.serialize_tensor:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.sign:7
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.sign:7
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.sign:7
msgid "The input tensor."
msgstr ""

#: of
#: tensornetwork.backends.abstract_backend.AbstractBackend.serialize_tensor:5
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.serialize_tensor:5
msgid "A string representing the serialized tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.set_random_state:1
#: tensorcircuit.backends.jax_backend.JaxBackend.set_random_state:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.set_random_state:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.set_random_state:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.set_random_state:1
msgid "Set the random state attached to the backend."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.set_random_state:3
#: tensorcircuit.backends.jax_backend.JaxBackend.set_random_state:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.set_random_state:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.set_random_state:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.set_random_state:3
msgid "the random seed, defaults to be None"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.set_random_state:5
#: tensorcircuit.backends.jax_backend.JaxBackend.set_random_state:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.set_random_state:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.set_random_state:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.set_random_state:5
msgid ""
"If set to be true, only get the random state in return instead of setting"
" the state on the backend"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.shape_concat:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.shape_concat:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.shape_concat:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.shape_concat:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.shape_concat:1
msgid "Concatenate a sequence of tensors together about the given axis."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.shape_prod:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.shape_prod:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.shape_prod:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.shape_prod:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.shape_prod:1
msgid "Take the product of all of the elements in values"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.shape_tensor:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.shape_tensor:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.shape_tensor:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.shape_tensor:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.shape_tensor:1
msgid "Get the shape of a tensor."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.shape_tensor:5
#: tensornetwork.backends.jax.jax_backend.JaxBackend.shape_tensor:5
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.shape_tensor:5
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.shape_tensor:5
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.shape_tensor:5
msgid "The shape of the input tensor returned as another tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.shape_tuple:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.shape_tuple:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.shape_tuple:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.shape_tuple:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.shape_tuple:1
msgid "Get the shape of a tensor as a tuple of integers."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.shape_tuple:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.shape_tuple:5
#: tensornetwork.backends.jax.jax_backend.JaxBackend.shape_tuple:5
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.shape_tuple:5
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.shape_tuple:5
msgid "The shape of the input tensor returned as a tuple of ints."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.sigmoid:1
#: tensorcircuit.backends.jax_backend.JaxBackend.sigmoid:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sigmoid:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sigmoid:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.sigmoid:1
msgid "Compute sigmoid of input ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.sign:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.sign:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.sign:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.sign:1
msgid ""
"Returns an elementwise tensor with entries y[i] = 1, 0, -1 where "
"tensor[i] > 0, == 0, and < 0 respectively."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.sin:1
#: tensorcircuit.backends.jax_backend.JaxBackend.sin:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sin:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sin:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.sin:1
msgid "Return sin of `tensor`. :param tensor: A tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.sinh:1
#: tensorcircuit.backends.jax_backend.JaxBackend.sinh:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sinh:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sinh:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.sinh:1
msgid "Return the sinh of a tensor ``a``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.sinh:5
#: tensorcircuit.backends.jax_backend.JaxBackend.sinh:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sinh:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sinh:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.sinh:5
msgid "sinh of ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.size:1
#: tensorcircuit.backends.jax_backend.JaxBackend.size:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.size:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.size:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.size:1
msgid "Return the total number of elements in ``a`` in tensor form."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.size:5
#: tensorcircuit.backends.jax_backend.JaxBackend.size:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.size:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.size:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.size:5
msgid "the total number of elements in ``a``"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.sizen:1
msgid "Return the total number of elements in tensor ``a``, but in integer form."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.sizen:5
msgid "the total number of elements in tensor ``a``"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.slice:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.slice:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.slice:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.slice:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.slice:1
msgid "Obtains a slice of a tensor based on start_indices and slice_sizes."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.slice:4
#: tensornetwork.backends.jax.jax_backend.JaxBackend.slice:4
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.slice:4
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.slice:4
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.slice:4
msgid "Tuple of integers denoting start indices of slice."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.slice:5
#: tensornetwork.backends.jax.jax_backend.JaxBackend.slice:5
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.slice:5
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.slice:5
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.slice:5
msgid "Tuple of integers denoting size of slice along each axis."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.softmax:1
#: tensorcircuit.backends.jax_backend.JaxBackend.softmax:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.softmax:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.softmax:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.softmax:1
msgid ""
"Softmax function. Computes the function which rescales elements to the "
"range [0,1] such that the elements along axis sum to 1."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.softmax:4
#: tensorcircuit.backends.jax_backend.JaxBackend.softmax:4
#: tensorcircuit.backends.numpy_backend.NumpyBackend.softmax:4
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.softmax:4
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.softmax:4
#, python-brace-format
msgid "\\mathrm{softmax}(x) = \\frac{\\exp(x_i)}{\\sum_j \\exp(x_j)}"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.softmax:11
#: tensorcircuit.backends.jax_backend.JaxBackend.softmax:11
#: tensorcircuit.backends.numpy_backend.NumpyBackend.softmax:11
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.softmax:11
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.softmax:11
msgid ""
"A dimension along which Softmax will be computed , defaults to None for "
"all axis sum."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.softmax:13
#: tensorcircuit.backends.cupy_backend.CuPyBackend.stack:7
#: tensorcircuit.backends.jax_backend.JaxBackend.softmax:13
#: tensorcircuit.backends.jax_backend.JaxBackend.stack:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.softmax:13
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stack:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.softmax:13
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stack:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.softmax:13
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stack:7
msgid "concatenated tensor"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.solve:1
#: tensorcircuit.backends.jax_backend.JaxBackend.solve:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.solve:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.solve:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.solve:1
msgid "Solve the linear system Ax=b and return the solution x."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.solve:3
#: tensorcircuit.backends.jax_backend.JaxBackend.solve:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.solve:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.solve:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.solve:3
msgid "The multiplied matrix."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.solve:5
#: tensorcircuit.backends.jax_backend.JaxBackend.solve:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.solve:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.solve:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.solve:5
msgid "The resulted matrix."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.solve:7
#: tensorcircuit.backends.jax_backend.JaxBackend.solve:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.solve:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.solve:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.solve:7
msgid "The solution of the linear system."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.sort:1
#: tensorcircuit.backends.jax_backend.JaxBackend.sort:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sort:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sort:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.sort:1
msgid "Sort a tensor along the given axis."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.sort:5
#: tensorcircuit.backends.jax_backend.JaxBackend.sort:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sort:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sort:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.sort:5
msgid "[description], defaults to -1"
msgstr ""

#: of
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.sparse_csr_from_coo:1
#: tensorcircuit.backends.jax_backend.JaxBackend.sparse_csr_from_coo:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sparse_csr_from_coo:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sparse_csr_from_coo:1
msgid "transform a coo matrix to a csr matrix"
msgstr ""

#: of
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.sparse_csr_from_coo:3
#: tensorcircuit.backends.jax_backend.JaxBackend.sparse_csr_from_coo:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sparse_csr_from_coo:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sparse_csr_from_coo:3
msgid "a coo matrix"
msgstr ""

#: of
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.sparse_csr_from_coo:5
#: tensorcircuit.backends.jax_backend.JaxBackend.sparse_csr_from_coo:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sparse_csr_from_coo:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sparse_csr_from_coo:5
msgid ""
"whether to enforce the transform, defaults to False, corresponding to "
"return the coo matrix if there is no implementation for specific backend."
msgstr ""

#: of
#: tensorcircuit.backends.abstract_backend.ExtendedBackend.sparse_csr_from_coo:8
#: tensorcircuit.backends.jax_backend.JaxBackend.sparse_csr_from_coo:8
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sparse_csr_from_coo:8
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sparse_csr_from_coo:8
msgid "a csr matrix"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.sparse_dense_matmul:1
#: tensorcircuit.backends.jax_backend.JaxBackend.sparse_dense_matmul:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sparse_dense_matmul:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sparse_dense_matmul:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.sparse_dense_matmul:1
msgid "A sparse matrix multiplies a dense matrix."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.sparse_dense_matmul:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.to_dense:3
#: tensorcircuit.backends.jax_backend.JaxBackend.sparse_dense_matmul:3
#: tensorcircuit.backends.jax_backend.JaxBackend.to_dense:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sparse_dense_matmul:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.to_dense:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sparse_dense_matmul:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.to_dense:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.sparse_dense_matmul:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.to_dense:3
msgid "a sparse matrix"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.sparse_dense_matmul:5
#: tensorcircuit.backends.jax_backend.JaxBackend.sparse_dense_matmul:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sparse_dense_matmul:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sparse_dense_matmul:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.sparse_dense_matmul:5
msgid "a dense matrix"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.sparse_dense_matmul:7
#: tensorcircuit.backends.jax_backend.JaxBackend.sparse_dense_matmul:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.sparse_dense_matmul:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.sparse_dense_matmul:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.sparse_dense_matmul:7
msgid "dense matrix"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.special_jv:1
#: tensorcircuit.backends.jax_backend.JaxBackend.special_jv:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.special_jv:1
msgid "Special function: Bessel function of the first kind."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.special_jv:3
#: tensorcircuit.backends.jax_backend.JaxBackend.special_jv:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.special_jv:3
msgid "The order of the Bessel function."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.special_jv:5
#: tensorcircuit.backends.jax_backend.JaxBackend.special_jv:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.special_jv:5
msgid "The argument of the Bessel function."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.special_jv:7
#: tensorcircuit.backends.jax_backend.JaxBackend.special_jv:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.special_jv:7
#, python-brace-format
msgid "The value of the Bessel function [J_0, ...J_{v-1}(z)]."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.sqrt:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.sqrt:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.sqrt:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.sqrt:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.sqrt:1
msgid "Take the square root (element wise) of a given tensor."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.sqrtmh:1
msgid "Return the sqrtm of a Hermitian matrix ``a``."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.sqrtmh:5
msgid ""
"whether the input ``a`` is guaranteed as a positive semidefinite matrix, "
"defaults False"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.sqrtmh:8
msgid "sqrtm of ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.stack:1
#: tensorcircuit.backends.jax_backend.JaxBackend.stack:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stack:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stack:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stack:1
msgid "Concatenates a sequence of tensors ``a`` along a new dimension ``axis``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.stack:3
#: tensorcircuit.backends.jax_backend.JaxBackend.stack:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stack:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stack:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stack:3
msgid "List of tensors in the same shape"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.stack:5
#: tensorcircuit.backends.jax_backend.JaxBackend.stack:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stack:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stack:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stack:5
msgid "the stack axis, defaults to 0"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randc:1
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randc:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randc:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randc:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randc:1
msgid "Categorical random sampler with given random state."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randc:3
#: tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randn:5
#: tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randu:3
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randc:3
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randn:5
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randu:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randc:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randn:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randu:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randc:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randn:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randu:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randc:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randn:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randu:3
msgid "stateful register for each package"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randn:1
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randn:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randn:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randn:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randn:1
msgid "Sample from a normal distribution with the given random state."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randn:3
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randn:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randn:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randn:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randn:3
msgid "The backend object"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randn:7
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randn:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randn:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randn:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randn:7
msgid "shape of output sampling tensor"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randn:13
#: tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randu:11
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randn:13
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randu:11
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randn:13
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randu:11
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randn:13
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randu:11
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randn:13
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randu:11
msgid ""
"only real data type is supported, \"32\" or \"64\", defaults to None "
"(global rdtypestr)"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randu:1
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randu:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randu:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randu:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randu:1
msgid "Uniform random sampler from ``low`` to ``high``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.stateful_randu:5
#: tensorcircuit.backends.jax_backend.JaxBackend.stateful_randu:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stateful_randu:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stateful_randu:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stateful_randu:5
msgid "shape of output sampling tensor, defaults to 1"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.std:1
#: tensorcircuit.backends.jax_backend.JaxBackend.std:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.std:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.std:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.std:1
msgid "Compute the standard deviation along the specified axis."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.std:5
#: tensorcircuit.backends.jax_backend.JaxBackend.std:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.std:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.std:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.std:5
msgid ""
"Axis or axes along which the standard deviation is computed, defaults to "
"None, implying all axis"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.std:8
#: tensorcircuit.backends.jax_backend.JaxBackend.std:8
#: tensorcircuit.backends.numpy_backend.NumpyBackend.std:8
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.std:8
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.std:8
msgid ""
"If this is set to True, the axes which are reduced are left in the result"
" as dimensions with size one, defaults to False"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.stop_gradient:1
#: tensorcircuit.backends.jax_backend.JaxBackend.stop_gradient:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.stop_gradient:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.stop_gradient:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.stop_gradient:1
msgid "Stop backpropagation from ``a``."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.subtraction:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.subtraction:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.subtraction:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.subtraction:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.subtraction:1
msgid ""
"Return the default substraction of `tensor`. A backend can override such "
"implementation. :param tensor1: A tensor. :param tensor2: A tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.sum:1
#: tensorcircuit.backends.numpy_backend._sum_numpy:1
#: tensorcircuit.backends.pytorch_backend._sum_torch:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.sum:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.sum:1
msgid ""
"Sum elements of `tensor` along the specified `axis`. Results in a new "
"Tensor with the summed axis removed. :param tensor: An input tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.sum:5
#: tensorcircuit.backends.numpy_backend._sum_numpy:5
#: tensorcircuit.backends.pytorch_backend._sum_torch:5
#: tensornetwork.backends.jax.jax_backend.JaxBackend.sum:5
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.sum:5
msgid ""
"The result of performing the summation. The order of the tensor   will be"
" reduced by 1."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.sum:7
#: tensorcircuit.backends.numpy_backend._sum_numpy:7
#: tensorcircuit.backends.pytorch_backend._sum_torch:7
#: tensornetwork.backends.jax.jax_backend.JaxBackend.sum:7
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.sum:7
msgid "The result of performing the summation. The order of the tensor"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.sum:8
#: tensorcircuit.backends.numpy_backend._sum_numpy:8
#: tensorcircuit.backends.pytorch_backend._sum_torch:8
#: tensornetwork.backends.jax.jax_backend.JaxBackend.sum:8
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.sum:8
msgid "will be reduced by 1."
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:1
#: tensorcircuit.backends.tensorflow_backend._svd_tf:1
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:1
msgid "Computes the singular value decomposition (SVD) of a tensor."
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:3
#: tensorcircuit.backends.tensorflow_backend._svd_tf:3
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:3
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:3
msgid ""
"The SVD is performed by treating the tensor as a matrix, with an "
"effective left (row) index resulting from combining the axes "
"`tensor.shape[:pivot_axis]` and an effective right (column) index "
"resulting from combining the axes `tensor.shape[pivot_axis:]`."
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:8
#: tensorcircuit.backends.tensorflow_backend._svd_tf:8
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:8
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:8
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:8
msgid ""
"For example, if `tensor` had a shape (2, 3, 4, 5) and `pivot_axis` was 2,"
" then `u` would have shape (2, 3, 6), `s` would have shape (6), and `vh` "
"would have shape (6, 4, 5)."
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:12
#: tensorcircuit.backends.tensorflow_backend._svd_tf:12
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:12
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:12
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:12
msgid ""
"If `max_singular_values` is set to an integer, the SVD is truncated to "
"keep at most this many singular values."
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:15
#: tensorcircuit.backends.tensorflow_backend._svd_tf:15
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:15
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:15
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:15
msgid ""
"If `max_truncation_error > 0`, as many singular values will be truncated "
"as possible, so that the truncation error (the norm of discarded singular"
" values) is at most `max_truncation_error`. If `relative` is set `True` "
"then `max_truncation_err` is understood relative to the largest singular "
"value."
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:21
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:21
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:21
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:21
msgid ""
"If both `max_singular_values` and `max_truncation_error` are specified, "
"the number of retained singular values will be `min(max_singular_values, "
"nsv_auto_trunc)`, where `nsv_auto_trunc` is the number of singular values"
" that must be kept to maintain a truncation error smaller than "
"`max_truncation_error`."
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:27
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:27
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:27
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:27
msgid "The output consists of three tensors `u, s, vh` such that: ```python"
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:29
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:29
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:29
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:29
msgid "u[i1,...,iN, j] * s[j] * vh[j, k1,...,kM] == tensor[i1,...,iN, k1,...,kM]"
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:30
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:30
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:30
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:30
msgid ""
"``` Note that the output ordering matches numpy.linalg.svd rather than "
"tf.svd."
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:33
#: tensorcircuit.backends.pytorch_backend._qr_torch:18
#: tensorcircuit.backends.pytorch_backend._rq_torch:18
#: tensorcircuit.backends.tensorflow_backend._qr_tf:18
#: tensorcircuit.backends.tensorflow_backend._rq_tf:18
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:33
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:33
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:33
msgid "A tensor to be decomposed."
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:34
#: tensorcircuit.backends.pytorch_backend._qr_torch:20
#: tensorcircuit.backends.pytorch_backend._rq_torch:20
#: tensorcircuit.backends.tensorflow_backend._qr_tf:20
#: tensorcircuit.backends.tensorflow_backend._rq_tf:20
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:34
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:34
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:34
msgid "Where to split the tensor's axes before flattening into a matrix."
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:36
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:36
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:36
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:36
msgid "The number of singular values to keep, or `None` to keep them all."
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:38
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:38
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:38
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:38
msgid "The maximum allowed truncation error or `None` to not do any truncation."
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:40
#: tensorcircuit.cons.split_rules:7
#: tensorcircuit.mps_base.FiniteMPS.apply_two_site_gate:24
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:40
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:40
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:40
msgid "Multiply `max_truncation_err` with the largest singular value."
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:42
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:42
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:42
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:42
msgid ""
"Left tensor factor. s: Vector of ordered singular values from largest to "
"smallest. vh: Right tensor factor. s_rest: Vector of discarded singular "
"values (length zero if no         truncation)."
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:42
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:42
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:42
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:42
msgid ""
"Left tensor factor. s: Vector of ordered singular values from largest to "
"smallest. vh: Right tensor factor. s_rest: Vector of discarded singular "
"values (length zero if no"
msgstr ""

#: of tensorcircuit.backends.jax_backend._svd_jax:46
#: tensorcircuit.backends.tensorflow_backend._svd_tf:49
#: tensornetwork.backends.abstract_backend.AbstractBackend.svd:46
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.svd:46
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.svd:46
msgid "truncation)."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.switch:1
#: tensorcircuit.backends.jax_backend.JaxBackend.switch:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.switch:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.switch:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.switch:1
msgid "``branches[index]()``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.tan:1
#: tensorcircuit.backends.jax_backend.JaxBackend.tan:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.tan:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tan:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.tan:1
msgid "Return the tan of a tensor ``a``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.tan:5
#: tensorcircuit.backends.jax_backend.JaxBackend.tan:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.tan:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tan:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.tan:5
msgid "tan of ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.tanh:1
#: tensorcircuit.backends.jax_backend.JaxBackend.tanh:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.tanh:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tanh:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.tanh:1
msgid "Return the tanh of a tensor ``a``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.tanh:5
#: tensorcircuit.backends.jax_backend.JaxBackend.tanh:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.tanh:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tanh:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.tanh:5
msgid "tanh of ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.tensordot:1
#: tensorcircuit.backends.tensorflow_backend._tensordot_tf:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.tensordot:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.tensordot:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.tensordot:1
msgid "Do a tensordot of tensors `a` and `b` over the given axes."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.tensordot:4
#: tensorcircuit.backends.tensorflow_backend._tensordot_tf:4
#: tensornetwork.backends.jax.jax_backend.JaxBackend.tensordot:4
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.tensordot:4
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.tensordot:4
msgid "Another tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.tensordot:5
#: tensorcircuit.backends.tensorflow_backend._tensordot_tf:5
#: tensornetwork.backends.jax.jax_backend.JaxBackend.tensordot:5
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.tensordot:5
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.tensordot:5
msgid "Two lists of integers. These values are the contraction axes."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.tile:1
#: tensorcircuit.backends.jax_backend.JaxBackend.tile:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.tile:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tile:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.tile:1
msgid "Constructs a tensor by tiling a given tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.tile:5
#: tensorcircuit.backends.jax_backend.JaxBackend.tile:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.tile:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tile:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.tile:5
msgid "1d tensor with length the same as the rank of ``a``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.to_dense:1
#: tensorcircuit.backends.jax_backend.JaxBackend.to_dense:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.to_dense:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.to_dense:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.to_dense:1
msgid "Convert a sparse matrix to dense tensor."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.to_dense:5
#: tensorcircuit.backends.jax_backend.JaxBackend.to_dense:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.to_dense:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.to_dense:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.to_dense:5
msgid "the resulted dense matrix"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.to_dlpack:1
#: tensorcircuit.backends.jax_backend.JaxBackend.to_dlpack:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.to_dlpack:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.to_dlpack:1
msgid "Transform the tensor ``a`` as a dlpack capsule"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.top_k:1
#: tensorcircuit.backends.jax_backend.JaxBackend.top_k:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.top_k:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.top_k:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.top_k:1
msgid "Find values and indices of the k largest elements of the last dimension."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.top_k:5
#: tensorcircuit.backends.jax_backend.JaxBackend.top_k:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.top_k:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.top_k:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.top_k:5
msgid "Number of top elements to find"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.top_k:7
#: tensorcircuit.backends.jax_backend.JaxBackend.top_k:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.top_k:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.top_k:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.top_k:7
msgid "(top_values, top_indices)"
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.trace:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.trace:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.trace:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.trace:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.trace:1
msgid "Return summed entries along diagonals."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.trace:3
#: tensornetwork.backends.jax.jax_backend.JaxBackend.trace:3
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.trace:3
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.trace:3
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.trace:3
msgid ""
"If tensor is 2-D, the sum is over the diagonal of tensor with the given "
"offset, i.e., the collection of elements of the form a[i, i+offset]. If a"
" has more than two dimensions, then the axes specified by axis1 and axis2"
" are used to determine the 2-D sub-array whose diagonal is summed."
msgstr ""

#: of tensornetwork.backends.abstract_backend.AbstractBackend.trace:19
#: tensornetwork.backends.jax.jax_backend.JaxBackend.trace:19
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.trace:19
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.trace:31
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.trace:28
msgid "The batched summed diagonals."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.transpose:1
#: tensornetwork.backends.jax.jax_backend.JaxBackend.transpose:1
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.transpose:1
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.transpose:1
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.transpose:1
msgid ""
"Transpose a tensor according to a given permutation. By default the axes "
"are reversed. :param tensor: A tensor. :param perm: The permutation of "
"the axes."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.transpose:6
#: tensornetwork.backends.jax.jax_backend.JaxBackend.transpose:6
#: tensornetwork.backends.numpy.numpy_backend.NumPyBackend.transpose:6
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.transpose:6
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.transpose:6
msgid "The transposed tensor"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.tree_flatten:1
#: tensorcircuit.backends.jax_backend.JaxBackend.tree_flatten:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tree_flatten:1
msgid "Flatten python structure to 1D list"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.tree_flatten:3
#: tensorcircuit.backends.jax_backend.JaxBackend.tree_flatten:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tree_flatten:3
msgid "python structure to be flattened"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.tree_flatten:5
#: tensorcircuit.backends.jax_backend.JaxBackend.tree_flatten:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tree_flatten:5
msgid ""
"The 1D list of flattened structure and treedef which can be used for "
"later unflatten"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.tree_map:1
#: tensorcircuit.backends.jax_backend.JaxBackend.tree_map:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tree_map:1
msgid "Return the new tree map with multiple arg function ``f`` through pytrees."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.tree_map:3
#: tensorcircuit.backends.jax_backend.JaxBackend.tree_map:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tree_map:3
msgid "The function"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.tree_map:5
#: tensorcircuit.backends.jax_backend.JaxBackend.tree_map:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tree_map:5
msgid "inputs as any python structure"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.tree_map:7
#: tensorcircuit.backends.jax_backend.JaxBackend.tree_map:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tree_map:7
msgid "raise when neither tensorflow or jax is installed."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.tree_map:8
#: tensorcircuit.backends.jax_backend.JaxBackend.tree_map:8
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tree_map:8
msgid "The new tree map with the same structure but different values."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.tree_unflatten:1
#: tensorcircuit.backends.jax_backend.JaxBackend.tree_unflatten:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tree_unflatten:1
msgid "Pack 1D list to pytree defined via ``treedef``"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.tree_unflatten:3
#: tensorcircuit.backends.jax_backend.JaxBackend.tree_unflatten:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tree_unflatten:3
msgid "Def of pytree structure, the second return from ``tree_flatten``"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.tree_unflatten:5
#: tensorcircuit.backends.jax_backend.JaxBackend.tree_unflatten:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tree_unflatten:5
msgid "the 1D list of flattened data structure"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.tree_unflatten:7
#: tensorcircuit.backends.jax_backend.JaxBackend.tree_unflatten:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.tree_unflatten:7
msgid "Packed pytree"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.unique_with_counts:1
#: tensorcircuit.backends.jax_backend.JaxBackend.unique_with_counts:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.unique_with_counts:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.unique_with_counts:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.unique_with_counts:1
msgid ""
"Find the unique elements and their corresponding counts of the given "
"tensor ``a``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.unique_with_counts:5
#: tensorcircuit.backends.jax_backend.JaxBackend.unique_with_counts:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.unique_with_counts:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.unique_with_counts:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.unique_with_counts:5
msgid "Unique elements, corresponding counts"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.value_and_grad:1
#: tensorcircuit.backends.jax_backend.JaxBackend.value_and_grad:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.value_and_grad:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.value_and_grad:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.value_and_grad:1
msgid "Return the function which returns the value and grad of ``f``."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.value_and_grad:17
#: tensorcircuit.backends.jax_backend.JaxBackend.value_and_grad:17
#: tensorcircuit.backends.numpy_backend.NumpyBackend.value_and_grad:17
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.value_and_grad:17
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.value_and_grad:17
msgid ""
"the value and grad function of ``f`` with the same set of arguments as "
"``f``"
msgstr ""

#: of
#: tensorcircuit.backends.cupy_backend.CuPyBackend.vectorized_value_and_grad:1
#: tensorcircuit.backends.jax_backend.JaxBackend.vectorized_value_and_grad:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vectorized_value_and_grad:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vectorized_value_and_grad:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vectorized_value_and_grad:1
msgid ""
"Return the VVAG function of ``f``. The inputs for ``f`` is (args[0], "
"args[1], args[2], ...), and the output of ``f`` is a scalar. Suppose "
"VVAG(f) is a function with inputs in the form (vargs[0], args[1], "
"args[2], ...), where vagrs[0] has one extra dimension than args[0] in the"
" first axis and consistent with args[0] in shape for remaining "
"dimensions, i.e. shape(vargs[0]) = [batch] + shape(args[0]). (We only "
"cover cases where ``vectorized_argnums`` defaults to 0 here for "
"demonstration). VVAG(f) returns a tuple as a value tensor with shape "
"[batch, 1] and a gradient tuple with shape: ([batch]+shape(args[argnum]) "
"for argnum in argnums). The gradient for argnums=k is defined as"
msgstr ""

#: of
#: tensorcircuit.backends.cupy_backend.CuPyBackend.vectorized_value_and_grad:9
#: tensorcircuit.backends.jax_backend.JaxBackend.vectorized_value_and_grad:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vectorized_value_and_grad:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vectorized_value_and_grad:9
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vectorized_value_and_grad:9
msgid ""
"g^k = \\frac{\\partial \\sum_{i\\in batch} f(vargs[0][i], args[1], "
"...)}{\\partial args[k]}"
msgstr ""

#: of
#: tensorcircuit.backends.cupy_backend.CuPyBackend.vectorized_value_and_grad:13
#: tensorcircuit.backends.jax_backend.JaxBackend.vectorized_value_and_grad:13
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vectorized_value_and_grad:13
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vectorized_value_and_grad:13
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vectorized_value_and_grad:13
msgid "Therefore, if argnums=0, the gradient is reduced to"
msgstr ""

#: of
#: tensorcircuit.backends.cupy_backend.CuPyBackend.vectorized_value_and_grad:15
#: tensorcircuit.backends.jax_backend.JaxBackend.vectorized_value_and_grad:15
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vectorized_value_and_grad:15
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vectorized_value_and_grad:15
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vectorized_value_and_grad:15
#, python-brace-format
msgid "g^0_i = \\frac{\\partial f(vargs[0][i])}{\\partial vargs[0][i]}"
msgstr ""

#: of
#: tensorcircuit.backends.cupy_backend.CuPyBackend.vectorized_value_and_grad:19
#: tensorcircuit.backends.jax_backend.JaxBackend.vectorized_value_and_grad:19
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vectorized_value_and_grad:19
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vectorized_value_and_grad:19
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vectorized_value_and_grad:19
msgid ""
", which is specifically suitable for batched VQE optimization, where "
"args[0] is the circuit parameters."
msgstr ""

#: of
#: tensorcircuit.backends.cupy_backend.CuPyBackend.vectorized_value_and_grad:21
#: tensorcircuit.backends.jax_backend.JaxBackend.vectorized_value_and_grad:21
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vectorized_value_and_grad:21
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vectorized_value_and_grad:21
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vectorized_value_and_grad:21
msgid "And if argnums=1, the gradient is like"
msgstr ""

#: of
#: tensorcircuit.backends.cupy_backend.CuPyBackend.vectorized_value_and_grad:23
#: tensorcircuit.backends.jax_backend.JaxBackend.vectorized_value_and_grad:23
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vectorized_value_and_grad:23
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vectorized_value_and_grad:23
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vectorized_value_and_grad:23
#, python-brace-format
msgid ""
"g^1_i = \\frac{\\partial \\sum_j f(vargs[0][j], args[1])}{\\partial "
"args[1][i]}\n"
"\n"
msgstr ""

#: of
#: tensorcircuit.backends.cupy_backend.CuPyBackend.vectorized_value_and_grad:26
#: tensorcircuit.backends.jax_backend.JaxBackend.vectorized_value_and_grad:26
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vectorized_value_and_grad:26
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vectorized_value_and_grad:26
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vectorized_value_and_grad:26
msgid ""
", which is suitable for quantum machine learning scenarios, where ``f`` "
"is the loss function, args[0] corresponds to the input data and args[1] "
"corresponds to the weights in the QML model."
msgstr ""

#: of
#: tensorcircuit.backends.cupy_backend.CuPyBackend.vectorized_value_and_grad:33
#: tensorcircuit.backends.cupy_backend.CuPyBackend.vmap:6
#: tensorcircuit.backends.jax_backend.JaxBackend.vectorized_value_and_grad:33
#: tensorcircuit.backends.jax_backend.JaxBackend.vmap:6
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vectorized_value_and_grad:33
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vmap:6
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vectorized_value_and_grad:33
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vmap:6
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vectorized_value_and_grad:33
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vmap:6
msgid ""
"the args to be vectorized, these arguments should share the same batch "
"shape in the fist dimension"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.vjp:1
#: tensorcircuit.backends.jax_backend.JaxBackend.vjp:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vjp:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vjp:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vjp:1
msgid ""
"Function that computes the dot product between a vector v and the "
"Jacobian of the given function at the point given by the inputs. (reverse"
" mode AD relevant) Strictly speaking, this function is value_and_vjp."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.vjp:5
#: tensorcircuit.backends.jax_backend.JaxBackend.vjp:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vjp:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vjp:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vjp:5
msgid "the function to carry out vjp calculation"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.vjp:9
#: tensorcircuit.backends.jax_backend.JaxBackend.vjp:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vjp:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vjp:9
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vjp:9
msgid ""
"value vector or gradient from downstream in reverse mode AD the same "
"shape as return of function ``f``"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.vjp:12
#: tensorcircuit.backends.jax_backend.JaxBackend.vjp:12
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vjp:12
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vjp:12
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vjp:12
msgid "(``f(*inputs)``, vjp_tensor), where vjp_tensor is the same shape as inputs"
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.vmap:1
#: tensorcircuit.backends.jax_backend.JaxBackend.vmap:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vmap:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vmap:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vmap:1
msgid ""
"Return the vectorized map or batched version of ``f`` on the first extra "
"axis. The general interface supports ``f`` with multiple arguments and "
"broadcast in the fist dimension."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.vmap:4
#: tensorcircuit.backends.jax_backend.JaxBackend.vmap:4
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vmap:4
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vmap:4
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vmap:4
msgid "function to be broadcasted."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.vmap:9
#: tensorcircuit.backends.jax_backend.JaxBackend.vmap:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.vmap:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.vmap:9
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.vmap:9
msgid "vmap version of ``f``"
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.where:1
#: tensorcircuit.backends.jax_backend.JaxBackend.where:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.where:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.where:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.where:1
msgid ""
"Return a tensor of elements selected from either x or y, depending on "
"condition."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.where:3
#: tensorcircuit.backends.jax_backend.JaxBackend.where:3
#: tensorcircuit.backends.numpy_backend.NumpyBackend.where:3
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.where:3
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.where:3
msgid "Where True, yield x, otherwise yield y."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.where:5
#: tensorcircuit.backends.jax_backend.JaxBackend.where:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.where:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.where:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.where:5
msgid "Values from which to choose when condition is True."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.where:7
#: tensorcircuit.backends.jax_backend.JaxBackend.where:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.where:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.where:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.where:7
msgid "Values from which to choose when condition is False."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.where:9
#: tensorcircuit.backends.jax_backend.JaxBackend.where:9
#: tensorcircuit.backends.numpy_backend.NumpyBackend.where:9
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.where:9
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.where:9
msgid "A tensor with elements from x where condition is True, and y otherwise."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.zeros:1
#: tensorcircuit.backends.jax_backend.JaxBackend.zeros:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.zeros:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.zeros:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.zeros:1
msgid ""
"Return a zeros-matrix of dimension `dim` Depending on specific backends, "
"`dim` has to be either an int (numpy, torch, tensorflow) or a `ShapeType`"
" object (for block-sparse backends)."
msgstr ""

#: of tensorcircuit.backends.cupy_backend.CuPyBackend.zeros:5
#: tensorcircuit.backends.jax_backend.JaxBackend.zeros:5
#: tensorcircuit.backends.numpy_backend.NumpyBackend.zeros:5
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.zeros:5
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.zeros:5
msgid ""
"Block-sparse behavior is currently not supported :param shape: The "
"dimension of the returned matrix. :type shape: int :param dtype: The "
"dtype of the returned matrix."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.zeros_like:1
#: tensorcircuit.backends.jax_backend.JaxBackend.zeros_like:1
#: tensorcircuit.backends.numpy_backend.NumpyBackend.zeros_like:1
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.zeros_like:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.zeros_like:1
msgid "Return a tensor of zeros with the same shape as a."
msgstr ""

#: of tensorcircuit.backends.abstract_backend.ExtendedBackend.zeros_like:7
#: tensorcircuit.backends.jax_backend.JaxBackend.zeros_like:7
#: tensorcircuit.backends.numpy_backend.NumpyBackend.zeros_like:7
#: tensorcircuit.backends.pytorch_backend.PyTorchBackend.zeros_like:7
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.zeros_like:7
msgid "A tensor of zeros with the same shape as a"
msgstr ""

#: ../../source/api/backends/jax_backend.rst:2
msgid "tensorcircuit.backends.jax_backend"
msgstr ""

#: of tensorcircuit.backends.jax_backend:1
msgid "Backend magic inherited from tensornetwork: jax backend"
msgstr ""

#: of tensorcircuit.backends.jax_backend.JaxBackend:1
msgid ""
"See the original backend API at `jax backend "
"<https://github.com/google/TensorNetwork/blob/master/tensornetwork/backends/jax/jax_backend.py>`_"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.diagonal:16
#: tensornetwork.backends.jax.jax_backend.JaxBackend.diagonal:19
#: tensornetwork.backends.jax.jax_backend.JaxBackend.trace:12
#: tensornetwork.backends.jax.jax_backend.JaxBackend.trace:15
msgid ""
"Axis to be used as the first/second axis of the 2D sub-arrays from which "
"the diagonals should be taken. Defaults to second last/last axis."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:1
msgid ""
"Implicitly restarted Arnoldi method for finding the lowest eigenvector-"
"eigenvalue pairs of a linear operator `A`. `A` is a function implementing"
" the matrix-vector product."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:6
msgid ""
"WARNING: This routine uses jax.jit to reduce runtimes. jitting is "
"triggered at the first invocation of `eigs`, and on any subsequent calls "
"if the python `id` of `A` changes, even if the formal definition of `A` "
"stays the same. Example: the following will jit once at the beginning, "
"and then never again:"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:12
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:12
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:10
msgid "```python import jax import numpy as np def A(H,x):"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:16
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:31
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:16
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:31
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:14
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:29
msgid "return jax.np.dot(H,x)"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:17
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:17
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:15
msgid "for n in range(100):"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:18
msgid ""
"H = jax.np.array(np.random.rand(10,10)) x = "
"jax.np.array(np.random.rand(10,10)) res = eigs(A, [H],x) #jitting is "
"triggerd only at `n=0`"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:23
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:23
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:21
msgid ""
"The following code triggers jitting at every iteration, which results in "
"considerably reduced performance"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:26
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:26
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:24
msgid "```python import jax import numpy as np for n in range(100):"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:30
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:30
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:28
msgid "def A(H,x):"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:32
msgid ""
"H = jax.np.array(np.random.rand(10,10)) x = "
"jax.np.array(np.random.rand(10,10)) res = eigs(A, [H],x) #jitting is "
"triggerd at every step `n`"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:37
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:37
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:35
msgid ""
"A (sparse) implementation of a linear operator. Call signature of `A` is "
"`res = A(vector, *args)`, where `vector` can be an arbitrary `Tensor`, "
"and `res.shape` has to be `vector.shape`."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:42
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:42
msgid ""
"An initial vector for the algorithm. If `None`, a random initial `Tensor`"
" is created using the `backend.randn` method"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:45
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:45
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:43
msgid ""
"The dtype of the input `A`. If no `initial_state` is provided, a random "
"initial state with shape `shape` and dtype `dtype` is created."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:48
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:48
msgid "The number of eigenvector-eigenvalue pairs to be computed."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:49
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:49
msgid ""
"The desired precision of the eigenvalues. For the jax backend this has "
"currently no effect, and precision of eigenvalues is not guaranteed. This"
" feature may be added at a later point. To increase precision the caller "
"can either increase `maxiter` or `num_krylov_vecs`."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:53
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:53
msgid ""
"Flag for targetting different types of eigenvalues. Currently supported "
"are `which = 'LR'` (larges real part) and `which = 'LM'` (larges "
"magnitude)."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:56
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:56
msgid ""
"Maximum number of restarts. For `maxiter=0` the routine becomes "
"equivalent to a simple Arnoldi method."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:59
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:59
msgid ""
"(eigvals, eigvecs)  eigvals: A list of `numeig` eigenvalues  eigvecs: A "
"list of `numeig` eigenvectors"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigs:62
#: tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:62
msgid ""
"eigvals: A list of `numeig` eigenvalues eigvecs: A list of `numeig` "
"eigenvectors"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:1
msgid ""
"Implicitly restarted Lanczos method for finding the lowest eigenvector-"
"eigenvalue pairs of a symmetric (hermitian) linear operator `A`. `A` is a"
" function implementing the matrix-vector product."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:6
msgid ""
"WARNING: This routine uses jax.jit to reduce runtimes. jitting is "
"triggered at the first invocation of `eigsh`, and on any subsequent calls"
" if the python `id` of `A` changes, even if the formal definition of `A` "
"stays the same. Example: the following will jit once at the beginning, "
"and then never again:"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:18
msgid ""
"H = jax.np.array(np.random.rand(10,10)) x = "
"jax.np.array(np.random.rand(10,10)) res = eigsh(A, [H],x) #jitting is "
"triggerd only at `n=0`"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh:32
msgid ""
"H = jax.np.array(np.random.rand(10,10)) x = "
"jax.np.array(np.random.rand(10,10)) res = eigsh(A, [H],x) #jitting is "
"triggerd at every step `n`"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:1
msgid ""
"Lanczos method for finding the lowest eigenvector-eigenvalue pairs of a "
"hermitian linear operator `A`. `A` is a function implementing the matrix-"
"vector product. WARNING: This routine uses jax.jit to reduce runtimes. "
"jitting is triggered at the first invocation of `eigsh_lanczos`, and on "
"any subsequent calls if the python `id` of `A` changes, even if the "
"formal definition of `A` stays the same. Example: the following will jit "
"once at the beginning, and then never again:"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:16
msgid ""
"H = jax.np.array(np.random.rand(10,10)) x = "
"jax.np.array(np.random.rand(10,10)) res = eigsh_lanczos(A, [H],x) "
"#jitting is triggerd only at `n=0`"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:30
msgid ""
"H = jax.np.array(np.random.rand(10,10)) x = "
"jax.np.array(np.random.rand(10,10)) res = eigsh_lanczos(A, [H],x) "
"#jitting is triggerd at every step `n`"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:46
msgid ""
"The number of eigenvector-eigenvalue pairs to be computed. If `numeig > "
"1`, `reorthogonalize` has to be `True`."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:48
msgid ""
"The desired precision of the eigenvalues. For the jax backend this has "
"currently no effect, and precision of eigenvalues is not guaranteed. This"
" feature may be added at a later point. To increase precision the caller "
"can increase `num_krylov_vecs`."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:57
msgid ""
"The tridiagonal Operator is diagonalized every `ndiag` iterations to "
"check convergence. This has currently no effect for the jax backend, but "
"may be added at a later point."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:63
msgid ""
"(eigvals, eigvecs)  eigvals: A jax-array containing `numeig` lowest "
"eigenvalues  eigvecs: A list of `numeig` lowest eigenvectors"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.eigsh_lanczos:66
msgid ""
"eigvals: A jax-array containing `numeig` lowest eigenvalues eigvecs: A "
"list of `numeig` lowest eigenvectors"
msgstr ""

#: of tensorcircuit.backends.jax_backend.JaxBackend.lobpcg_standard:1
msgid "Compute top-k eigenpairs via LOBPCG for Hermitian matrices."
msgstr ""

#: of tensorcircuit.backends.jax_backend.JaxBackend.meshgrid:1
#: tensorcircuit.backends.tensorflow_backend.TensorFlowBackend.meshgrid:1
msgid "Backend-agnostic meshgrid function."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.power:1
msgid ""
"Returns the power of tensor a to the value of b. In the case b is a "
"tensor, then the power is by element"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.power:3
msgid "with a as the base and b as the exponent."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.power:4
msgid "In the case b is a scalar, then the power of each value in a"
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.power:5
msgid "is raised to the exponent of b."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.power:7
msgid "The tensor that contains the base."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.power:8
msgid "The tensor that contains the exponent or a single scalar."
msgstr ""

#: of tensorcircuit.backends.jax_backend._qr_jax:1
msgid ""
"Computes the QR decomposition of a tensor. See "
"tensornetwork.backends.tensorflow.decompositions for details."
msgstr ""

#: of tensorcircuit.backends.jax_backend._rq_jax:1
msgid ""
"Computes the RQ (reversed QR) decomposition of a tensor. See "
"tensornetwork.backends.tensorflow.decompositions for details."
msgstr ""

#: of tensornetwork.backends.jax.jax_backend.JaxBackend.sign:4
msgid ""
"For complex input the behaviour of this function may depend on the "
"backend. The Jax backend version returns y[i] = x[i]/sqrt(x[i]^2)."
msgstr ""

#: of tensorcircuit.backends.jax_backend.bcsr_scalar_mul:1
msgid "Implements scalar multiplication for BCSR matrices (self * scalar)."
msgstr ""

#: ../../source/api/backends/numpy_backend.rst:2
msgid "tensorcircuit.backends.numpy_backend"
msgstr ""

#: of tensorcircuit.backends.numpy_backend:1
msgid "Backend magic inherited from tensornetwork: numpy backend"
msgstr ""

#: of tensorcircuit.backends.numpy_backend.NumpyBackend:1
msgid ""
"see the original backend API at `numpy backend "
"<https://github.com/google/TensorNetwork/blob/master/tensornetwork/backends/numpy/numpy_backend.py>`_"
msgstr ""

#: of tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigs:1
msgid ""
"Arnoldi method for finding the lowest eigenvector-eigenvalue pairs of a "
"linear operator `A`. If no `initial_state` is provided then `shape` and "
"`dtype` are required so that a suitable initial state can be randomly "
"generated. This is a wrapper for scipy.sparse.linalg.eigs which only "
"supports a subset of the arguments of scipy.sparse.linalg.eigs."
msgstr ""

#: of tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigs:8
msgid "A (sparse) implementation of a linear operator"
msgstr ""

#: of tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigs:21
msgid ""
"['LM' | 'SM' | 'LR' | 'SR' | 'LI'] Which `k` eigenvectors and eigenvalues"
" to find:     'LM' : largest magnitude     'SM' : smallest magnitude     "
"'LR' : largest real part     'SR' : smallest real part     'LI' : largest"
" imaginary part"
msgstr ""

#: of tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigs:21
msgid ""
"['LM' | 'SM' | 'LR' | 'SR' | 'LI'] Which `k` eigenvectors and eigenvalues"
" to find:"
msgstr ""

#: of tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigs:23
msgid ""
"'LM' : largest magnitude 'SM' : smallest magnitude 'LR' : largest real "
"part 'SR' : smallest real part 'LI' : largest imaginary part"
msgstr ""

#: of tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigs:32
msgid "`np.ndarray`"
msgstr ""

#: of tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh_lanczos:1
msgid ""
"Lanczos method for finding the lowest eigenvector-eigenvalue pairs of a "
"linear operator `A`. :param A: A (sparse) implementation of a linear "
"operator."
msgstr ""

#: of tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh_lanczos:16
msgid ""
"The desired precision of the eigenvalus. Uses "
"`np.linalg.norm(eigvalsnew[0:numeig] - eigvalsold[0:numeig]) < tol` as "
"stopping criterion between two diagonalization steps of the tridiagonal "
"operator."
msgstr ""

#: of tensornetwork.backends.numpy.numpy_backend.NumPyBackend.eigsh_lanczos:25
msgid ""
"The tridiagonal Operator is diagonalized every `ndiag` iterations by "
"explicit orthogonalization (more costly than `reorthogonalize=False`)"
msgstr ""

#: of tensornetwork.backends.numpy.numpy_backend.NumPyBackend.sign:1
msgid ""
"Returns an elementwise tensor with entries y[i] = 1, 0, -1 tensor[i] > 0,"
" == 0, and < 0 respectively."
msgstr ""

#: of tensornetwork.backends.numpy.numpy_backend.NumPyBackend.sign:4
msgid ""
"For complex input the behaviour of this function may depend on the "
"backend. The NumPy version returns y[i] = x[i]/sqrt(x[i]^2)."
msgstr ""

#: ../../source/api/backends/pytorch_backend.rst:2
msgid "tensorcircuit.backends.pytorch_backend"
msgstr ""

#: of tensorcircuit.backends.pytorch_backend:1
msgid "Backend magic inherited from tensornetwork: pytorch backend"
msgstr ""

#: of tensorcircuit.backends.pytorch_backend.PyTorchBackend:1
msgid ""
"See the original backend API at `pytorch backend "
"<https://github.com/google/TensorNetwork/blob/master/tensornetwork/backends/pytorch/pytorch_backend.py>`_"
msgstr ""

#: of tensorcircuit.backends.pytorch_backend.PyTorchBackend:4
msgid ""
"Note the functionality provided by pytorch backend is incomplete, it "
"currenly lacks native efficicent jit and vmap support."
msgstr ""

#: of tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.diagonal:16
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.diagonal:20
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.diagonal:16
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.diagonal:24
msgid ""
"Axis to be used as the first/second axis of the 2D sub-arrays from which "
"the diagonals should be taken. Defaults to second-last and last axis "
"(note this differs from the NumPy defaults)."
msgstr ""

#: of
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eigsh_lanczos:1
msgid ""
"Lanczos method for finding the lowest eigenvector-eigenvalue pairs of a "
"`LinearOperator` `A`. :param A: A (sparse) implementation of a linear "
"operator."
msgstr ""

#: of
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eigsh_lanczos:8
msgid ""
"An initial vector for the Lanczos algorithm. If `None`, a random initial "
"`Tensor` is created using the `torch.randn` method"
msgstr ""

#: of
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.eigsh_lanczos:16
msgid ""
"The desired precision of the eigenvalus. Uses "
"`torch.norm(eigvalsnew[0:numeig] - eigvalsold[0:numeig]) < tol` as "
"stopping criterion between two diagonalization steps of the tridiagonal "
"operator."
msgstr ""

#: of tensorcircuit.backends.pytorch_backend.PyTorchBackend.lobpcg_standard:1
msgid ""
"PyTorch LOBPCG implementation. Note: 1. Complex input is not officially "
"supported and is numerically unstable. 2. Callable input for operator 'a'"
" is not supported."
msgstr ""

#: of tensorcircuit.backends.pytorch_backend._qr_torch:1
#: tensorcircuit.backends.tensorflow_backend._qr_tf:1
msgid ""
"Computes the QR decomposition of a tensor. The QR decomposition is "
"performed by treating the tensor as a matrix, with an effective left "
"(row) index resulting from combining the axes `tensor.shape[:pivot_axis]`"
" and an effective right (column) index resulting from combining the axes "
"`tensor.shape[pivot_axis:]`."
msgstr ""

#: of tensorcircuit.backends.pytorch_backend._qr_torch:9
#: tensorcircuit.backends.tensorflow_backend._qr_tf:9
msgid ""
"If `tensor` had a shape (2, 3, 4, 5) and `pivot_axis` was 2, then `q` "
"would have shape (2, 3, 6), and `r` would have shape (6, 4, 5). The "
"output consists of two tensors `Q, R` such that:"
msgstr ""

#: of tensorcircuit.backends.pytorch_backend._qr_torch:14
#: tensorcircuit.backends.pytorch_backend._rq_torch:14
#: tensorcircuit.backends.tensorflow_backend._qr_tf:14
#: tensorcircuit.backends.tensorflow_backend._rq_tf:14
msgid "Q[i1,...,iN, j] * R[j, k1,...,kM] == tensor[i1,...,iN, k1,...,kM]"
msgstr ""

#: of tensorcircuit.backends.pytorch_backend._qr_torch:16
#: tensorcircuit.backends.pytorch_backend._rq_torch:16
#: tensorcircuit.backends.tensorflow_backend._qr_tf:16
#: tensorcircuit.backends.tensorflow_backend._rq_tf:16
msgid "Note that the output ordering matches numpy.linalg.svd rather than tf.svd."
msgstr ""

#: of tensorcircuit.backends.pytorch_backend._qr_torch:22
#: tensorcircuit.backends.pytorch_backend._rq_torch:22
#: tensorcircuit.backends.tensorflow_backend._qr_tf:22
#: tensorcircuit.backends.tensorflow_backend._rq_tf:22
msgid "a bool indicating whether the tenor is diagonal non-negative matrix."
msgstr ""

#: of tensorcircuit.backends.pytorch_backend._qr_torch:24
#: tensorcircuit.backends.pytorch_backend._rq_torch:24
#: tensorcircuit.backends.tensorflow_backend._qr_tf:24
#: tensorcircuit.backends.tensorflow_backend._rq_tf:24
msgid "Q, the left tensor factor, and R, the right tensor factor."
msgstr ""

#: of tensorcircuit.backends.pytorch_backend._rq_torch:1
#: tensorcircuit.backends.tensorflow_backend._rq_tf:1
msgid ""
"Computes the RQ decomposition of a tensor. The QR decomposition is "
"performed by treating the tensor as a matrix, with an effective left "
"(row) index resulting from combining the axes `tensor.shape[:pivot_axis]`"
" and an effective right (column) index resulting from combining the axes "
"`tensor.shape[pivot_axis:]`."
msgstr ""

#: of tensorcircuit.backends.pytorch_backend._rq_torch:9
#: tensorcircuit.backends.tensorflow_backend._rq_tf:9
msgid ""
"If `tensor` had a shape (2, 3, 4, 5) and `pivot_axis` was 2, then `r` "
"would have shape (2, 3, 6), and `q` would have shape (6, 4, 5). The "
"output consists of two tensors `Q, R` such that:"
msgstr ""

#: of tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.sign:4
msgid ""
"For complex input the behaviour of this function may depend on the "
"backend. The PyTorch version is not implemented in this case."
msgstr ""

#: of tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.trace:10
msgid ""
"In the PyTorch backend the trace is always over the main diagonal of the "
"last two entries."
msgstr ""

#: of tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.trace:14
msgid ""
"Offset of the diagonal from the main diagonal. This argument is not "
"supported  by the PyTorch backend and an error will be raised if they are"
" specified."
msgstr ""

#: of tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.trace:18
#: tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend.trace:24
msgid ""
"Axis to be used as the first/second axis of the 2D sub-arrays from which "
"the diagonals should be taken. Defaults to first/second axis. These "
"arguments are not supported by the PyTorch backend and an error will be "
"raised if they are specified."
msgstr ""

#: of tensorcircuit.backends.pytorch_backend.torch_jit_func:1
msgid ""
"Delay the tracing of torch jit to the first run time: consistent with tf "
"and jax mechanism"
msgstr ""

#: ../../source/api/backends/tensorflow_backend.rst:2
msgid "tensorcircuit.backends.tensorflow_backend"
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend:1
msgid "Backend magic inherited from tensornetwork: tensorflow backend"
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend.TensorFlowBackend:1
msgid ""
"See the original backend API at `tensorflow backend "
"<https://github.com/google/TensorNetwork/blob/master/tensornetwork/backends/tensorflow/tensorflow_backend.py>`_"
msgstr ""

#: of
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.diagonal:16
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.diagonal:24
msgid ""
"Axis to be used as the first/second axis of the 2D sub-arrays from which "
"the diagonals should be taken. Defaults to second-last and last axis "
"(note this differs from the NumPy defaults).  These arguments are not "
"supported in the TensorFlow backend and an error will be raised if they "
"are specified."
msgstr ""

#: of
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.diagonal:21
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.diagonal:29
msgid ""
"These arguments are not supported in the TensorFlow backend and an error "
"will be raised if they are specified."
msgstr ""

#: of
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.sign:4
msgid ""
"For complex input the behaviour of this function may depend on the "
"backend. The TensorFlow version returns y[i] = x[i] / abs(x[i])."
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend._svd_tf:21
msgid ""
"If both `max_singular_values` snd `max_truncation_error` are specified, "
"the number of retained singular values will be `min(max_singular_values, "
"nsv_auto_trunc)`, where `nsv_auto_trunc` is the number of singular values"
" that must be kept to maintain a truncation error smaller than "
"`max_truncation_error`."
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend._svd_tf:27
msgid ""
"The output consists of three tensors `u, s, vh` such that: ```python "
"u[i1,...,iN, j] * s[j] * vh[j, k1,...,kM] == tensor[i1,...,iN, k1,...,kM]"
" ``` Note that the output ordering matches numpy.linalg.svd rather than "
"tf.svd."
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend._svd_tf:33
msgid ""
"Args: tf: The tensorflow module. tensor: A tensor to be decomposed. "
"pivot_axis: Where to split the tensor's axes before flattening into a"
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend._svd_tf:37
msgid "matrix."
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend._svd_tf:38
msgid "max_singular_values: The number of singular values to keep, or `None` to"
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend._svd_tf:39
msgid "keep them all."
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend._svd_tf:40
msgid ""
"max_truncation_error: The maximum allowed truncation error or `None` to "
"not"
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend._svd_tf:41
msgid "do any truncation."
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend._svd_tf:42
msgid "relative: Multiply `max_truncation_err` with the largest singular value."
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend._svd_tf:44
msgid ""
"Returns: u: Left tensor factor. s: Vector of ordered singular values from"
" largest to smallest. vh: Right tensor factor. s_rest: Vector of "
"discarded singular values (length zero if no"
msgstr ""

#: of
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.trace:11
msgid ""
"Offset of the diagonal from the main diagonal. This argument is not "
"supported in the TensorFlow backend and an error will be raised if they "
"are specified."
msgstr ""

#: of
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.trace:15
#: tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend.trace:21
msgid ""
"Axis to be used as the first/second axis of the 2D sub-arrays from which "
"the diagonals should be taken. Defaults to first/second axis. These "
"arguments are not supported in the TensorFlow backend and an error will "
"be raised if they are specified."
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend.sparse_tensor_matmul:1
msgid "An implementation of matrix multiplication (@) for tf.SparseTensor."
msgstr ""

#: of tensorcircuit.backends.tensorflow_backend.sparse_tensor_matmul:3
msgid ""
"This function is designed to be monkey-patched onto the tf.SparseTensor "
"class. It handles multiplication with a dense vector (rank-1 Tensor) by "
"temporarily promoting it to a matrix (rank-2 Tensor) for the underlying "
"TensorFlow call."
msgstr ""

#: ../../source/api/basecircuit.rst:2
msgid "tensorcircuit.basecircuit"
msgstr ""

#: of tensorcircuit.basecircuit:1
msgid "Quantum circuit: common methods for all circuit classes as MixIn"
msgstr ""

#: of tensorcircuit.basecircuit:5
msgid "Supports qubit (d = 2) and qudit (d >= 2) systems."
msgstr ""

#: of tensorcircuit.basecircuit:6
msgid ""
"For string-encoded samples/counts when d <= 36, digits use base-d "
"characters 0-9A-Z (A = 10, ..., Z = 35)."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.amplitude:1
msgid ""
"Returns the amplitude of the circuit given the bitstring l. For state "
"simulator, it computes :math:`\\langle l\\vert \\psi\\rangle`, for "
"density matrix simulator, it computes :math:`Tr(\\rho \\vert l\\rangle "
"\\langle 1\\vert)` Note how these two are different up to a square "
"operation."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.amplitude:16
#: tensorcircuit.basecircuit.BaseCircuit.amplitude_before:6
msgid "The bitstring of 0 and 1s."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.amplitude:18
msgid "The amplitude of the circuit."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.amplitude_before:1
msgid ""
"Returns the tensornetwor nodes for the amplitude of the circuit given the"
" bitstring l. For state simulator, it computes :math:`\\langle l\\vert "
"\\psi\\rangle`, for density matrix simulator, it computes :math:`Tr(\\rho"
" \\vert l\\rangle \\langle 1\\vert)` Note how these two are different up "
"to a square operation."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.amplitude_before:8
#: tensorcircuit.quditcircuit.QuditCircuit.amplitude_before:9
msgid "The tensornetwork nodes for the amplitude of the circuit."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.coloring_copied_nodes:1
msgid ""
"Tag copied nodes while preserving the original node's identity for "
"lightcone cancellation."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.coloring_copied_nodes:3
msgid "A sequence of newly copied nodes."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.coloring_copied_nodes:5
msgid "The sequence of original nodes from which `nodes` were copied."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.coloring_copied_nodes:7
msgid "Whether the copied nodes represent conjugate operations, defaults to True."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.coloring_copied_nodes:10
msgid "A label for the node type, defaults to \"inputs\"."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.coloring_nodes:1
msgid ""
"Tag nodes with metadata used for casual lightcone simplification and "
"tracing."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.coloring_nodes:3
msgid "A sequence of tensornetwork nodes to tag."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.coloring_nodes:5
msgid ""
"Whether the nodes represent conjugate operations (U^\\dagger), defaults "
"to False."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.coloring_nodes:8
msgid ""
"A label for the node type (e.g., \"gate\", \"inputs\", \"operator\"), "
"defaults to \"inputs\"."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.cond_measurement:22
msgid "the site index for the Z-basis measurement"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.cond_measurement:24
msgid "0 or 1 for Z-basis measurement outcome"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.copy_nodes:1
msgid "copy all nodes and dangling edges correspondingly"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.detector_probabilities:1
msgid ""
"Calculate the joint probability distribution of all detectors in the "
"circuit."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.detector_probabilities:3
msgid "A tensor representing the joint probability distribution."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.expectation_before:1
msgid ""
"Get the tensor network in the form of a list of nodes for the expectation"
" calculation before the real contraction"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.expectation_before:4
#: tensorcircuit.interfaces.torch.torch_interface_kws:24
#: tensorcircuit.quditcircuit.QuditCircuit.expectation_before:3
#: tensorcircuit.utils.benchmark:7
msgid "_description_, defaults to True"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.get_quvector:1
#: tensorcircuit.quditcircuit.QuditCircuit.get_quvector:1
msgid ""
"Get the representation of the output state in the form of ``QuVector`` "
"while maintaining the circuit uncomputed"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.get_quvector:4
#: tensorcircuit.mpscircuit.MPSCircuit.get_quvector:4
#: tensorcircuit.quditcircuit.QuditCircuit.get_quvector:4
msgid "``QuVector`` representation of the output state from the circuit"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.measure_jit:1
msgid ""
"Take measurement on the given site indices (computational basis). This "
"method is jittable is and about 100 times faster than unjit version!"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.outcome_probability:1
msgid "Calculate the probability of a specific detector outcome bitstring."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.outcome_probability:3
msgid "The detector outcome bitstring as a sequence of 0s and 1s."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.outcome_probability:5
msgid "The probability of the given outcome."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.perfect_sampling:1
msgid ""
"Sampling base-d strings (0-9A-Z when d <= 36) from the circuit output "
"based on quantum amplitudes. Reference: arXiv:1201.3974."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.perfect_sampling:4
msgid "external randomness, with shape [nqubits], defaults to None"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.perfect_sampling:6
msgid "Sampled base-d string and the corresponding theoretical probability."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.probability:1
msgid "get the d^n length probability vector over computational basis"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.probability:3
msgid "probability vector of shape [dim**n]"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.projected_subsystem:1
msgid ""
"remaining wavefunction or density matrix on sites in ``left``, with other"
" sites fixed to given digits (0..d-1) as indicated by ``traceout``"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.projected_subsystem:4
msgid "can be jitted"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.projected_subsystem:6
msgid "cannot be jitted"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.readouterror_bs:1
msgid ""
"Apply readout error to original probabilities of bit string and return "
"the noisy probabilities."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.readouterror_bs:10
msgid "list of readout error for each qubits."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.readouterror_bs:12
msgid "probabilities of bit string"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.replace_inputs:1
#: tensorcircuit.quditcircuit.QuditCircuit.replace_inputs:1
msgid "Replace the input state with the circuit structure unchanged."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.replace_inputs:3
#: tensorcircuit.quditcircuit.QuditCircuit.replace_inputs:3
msgid "Input wavefunction."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample:1
msgid "batched sampling from state or circuit tensor network directly"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample:3
msgid "number of samples, defaults to None"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample:5
msgid ""
"if true, we sample from the final state if memory allows, True is "
"preferred, defaults to False"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample:8
msgid "readout_error, defaults to None"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample:10
#, python-brace-format
msgid ""
"sample format, defaults to None as backward compatibility check the doc "
"in :py:meth:`tensorcircuit.quantum.measurement_results` Six formats of "
"measurement counts results:      \"sample_int\": # np.array([0, 0])      "
"\"sample_bin\": # [np.array([1, 0]), np.array([1, 0])]      "
"\"count_vector\": # np.array([2, 0, 0, 0])      \"count_tuple\": # "
"(np.array([0]), np.array([2]))      \"count_dict_bin\": # {\"00\": 2, "
"\"01\": 0, \"10\": 0, \"11\": 0}         for cases d\\in [11, 36], use "
"0-9A-Z digits (e.g., 'A' -> 10, ..., 'Z' -> 35);      \"count_dict_int\":"
" # {0: 2, 1: 0, 2: 0, 3: 0}"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample:10
msgid ""
"sample format, defaults to None as backward compatibility check the doc "
"in :py:meth:`tensorcircuit.quantum.measurement_results` Six formats of "
"measurement counts results:"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample:14
#: tensorcircuit.quantum.measurement_counts:6
msgid "\"sample_int\": # np.array([0, 0])"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample:16
#: tensorcircuit.quantum.measurement_counts:8
msgid "\"sample_bin\": # [np.array([1, 0]), np.array([1, 0])]"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample:18
#: tensorcircuit.quantum.measurement_counts:10
#: tensorcircuit.quditcircuit.QuditCircuit.sample:12
msgid "\"count_vector\": # np.array([2, 0, 0, 0])"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample:20
#: tensorcircuit.quantum.measurement_counts:12
msgid "\"count_tuple\": # (np.array([0]), np.array([2]))"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample:22
#: tensorcircuit.quditcircuit.QuditCircuit.sample:14
#, python-brace-format
msgid "\"count_dict_bin\": # {\"00\": 2, \"01\": 0, \"10\": 0, \"11\": 0}"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample:23
msgid ""
"for cases d\\in [11, 36], use 0-9A-Z digits (e.g., 'A' -> 10, ..., 'Z' ->"
" 35);"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample:25
#: tensorcircuit.quantum.measurement_counts:17
#, python-brace-format
msgid "\"count_dict_int\": # {0: 2, 1: 0, 2: 0, 3: 0}"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample:32
msgid ""
"external randomness given by tensor uniformly from [0, 1], if set, can "
"overwrite random_generator, shape [batch] for `allow_state=True` and "
"shape [batch, nqubits] for `allow_state=False` using perfect sampling "
"implementation"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample_detector:1
msgid "Sample detector outcomes from instruction-annotated circuits."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample_detector:3
#: tensorcircuit.zx.noise_model.ChannelSampler.sample:5
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.sample_detectors:3
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.sample_measurements:3
msgid "Number of samples to draw, defaults to 1."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample_detector:5
msgid ""
"Number of samples to process in a single batch, defaults to None (equal "
"to shots)."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample_detector:7
msgid ""
"If True, uses the full detector probability distribution for sampling "
"(faster but memory-intensive); if False, uses an autoregressive sampling "
"method based on the tensor network, defaults to False."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample_detector:11
msgid ""
"Random numbers in [0, 1] used for sampling, defaults to None. If "
"allow_state is True, shape should be [shots] or [shots, 1]; if "
"allow_state is False, shape should be [shots, num_detectors]."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample_detector:15
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.__init__:5
msgid "Random seed for sampling, defaults to None."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample_detector:17
msgid ""
"A boolean tensor containing the sampled detector outcomes with shape "
"[shots, num_detectors]."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample_expectation_ps:1
msgid ""
"Compute the expectation with given Pauli string with measurement shots "
"numbers"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample_expectation_ps:31
msgid "index for Pauli X, defaults to None"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample_expectation_ps:33
msgid "index for Pauli Y, defaults to None"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample_expectation_ps:35
msgid "index for Pauli Z, defaults to None"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample_expectation_ps:37
#: tensorcircuit.noisemodel.sample_expectation_ps_noisfy:15
msgid ""
"number of measurement shots, defaults to None, indicating analytical "
"result"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample_expectation_ps:39
#: tensorcircuit.quantum.measurement_counts:51
msgid "random_generator, defaults to None"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample_expectation_ps:41
#: tensorcircuit.quantum.measurement_counts:53
msgid ""
"external randomness given by tensor uniformly from [0, 1], if set, can "
"overwrite random_generator"
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.sample_expectation_ps:44
msgid "readout_error, defaults to None. Overrided if noise_conf is provided."
msgstr ""

#: of tensorcircuit.basecircuit.BaseCircuit.to_graphviz:1
msgid ""
"Not an ideal visualization for quantum circuit, but reserve here as a "
"general approach to show the tensornetwork [Deprecated, use "
"``Circuit.vis_tex`` or ``Circuit.draw`` instead]"
msgstr ""

#: ../../source/api/channels.rst:2
msgid "tensorcircuit.channels"
msgstr ""

#: of tensorcircuit.channels:1
msgid "Some common noise quantum channels."
msgstr ""

#: of tensorcircuit.channels.KrausList.append:1
msgid "Append object to the end of the list."
msgstr ""

#: of tensorcircuit.channels.KrausList.clear:1
msgid "Remove all items from list."
msgstr ""

#: of tensorcircuit.channels.KrausList.copy:1
msgid "Return a shallow copy of the list."
msgstr ""

#: of tensorcircuit.channels.KrausList.count:1
#: tensorcircuit.zx.evaluator.ExactScalarArray.count:1
#: tensorcircuit.zx.scalar_graph.CompiledComponent.count:1
#: tensorcircuit.zx.scalar_graph.CompiledProgram.count:1
#: tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.count:1
msgid "Return number of occurrences of value."
msgstr ""

#: of tensorcircuit.channels.KrausList.extend:1
msgid "Extend list by appending elements from the iterable."
msgstr ""

#: of tensorcircuit.channels.KrausList.index:1
#: tensorcircuit.zx.evaluator.ExactScalarArray.index:1
#: tensorcircuit.zx.scalar_graph.CompiledComponent.index:1
#: tensorcircuit.zx.scalar_graph.CompiledProgram.index:1
#: tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.index:1
msgid "Return first index of value."
msgstr ""

#: of tensorcircuit.channels.KrausList.index:3
#: tensorcircuit.channels.KrausList.remove:3
#: tensorcircuit.zx.evaluator.ExactScalarArray.index:3
#: tensorcircuit.zx.scalar_graph.CompiledComponent.index:3
#: tensorcircuit.zx.scalar_graph.CompiledProgram.index:3
#: tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.index:3
msgid "Raises ValueError if the value is not present."
msgstr ""

#: of tensorcircuit.channels.KrausList.insert:1
msgid "Insert object before index."
msgstr ""

#: of tensorcircuit.channels.KrausList.pop:1
msgid "Remove and return item at index (default last)."
msgstr ""

#: of tensorcircuit.channels.KrausList.pop:3
msgid "Raises IndexError if list is empty or index is out of range."
msgstr ""

#: of tensorcircuit.channels.KrausList.remove:1
msgid "Remove first occurrence of value."
msgstr ""

#: of tensorcircuit.channels.KrausList.reverse:1
msgid "Reverse *IN PLACE*."
msgstr ""

#: of tensorcircuit.channels.KrausList.sort:1
msgid "Sort the list in ascending order and return None."
msgstr ""

#: of tensorcircuit.channels.KrausList.sort:3
msgid ""
"The sort is in-place (i.e. the list itself is modified) and stable (i.e. "
"the order of two equal elements is maintained)."
msgstr ""

#: of tensorcircuit.channels.KrausList.sort:6
msgid ""
"If a key function is given, apply it once to each list item and sort "
"them, ascending or descending, according to their function values."
msgstr ""

#: of tensorcircuit.channels.KrausList.sort:9
msgid "The reverse flag can be set to sort in descending order."
msgstr ""

#: of tensorcircuit.channels.amplitudedampingchannel:1
msgid ""
"Return an amplitude damping channel. Notice: Amplitude damping "
"corrspondings to p = 1."
msgstr ""

#: of tensorcircuit.channels.amplitudedampingchannel:4
#, python-brace-format
msgid ""
"\\sqrt{p}\n"
"\\begin{bmatrix}\n"
"    1 & 0\\\\\n"
"    0 & \\sqrt{1-\\gamma}\\\\\n"
"\\end{bmatrix}\\qquad\n"
"\\sqrt{p}\n"
"\\begin{bmatrix}\n"
"    0 & \\sqrt{\\gamma}\\\\\n"
"    0 & 0\\\\\n"
"\\end{bmatrix}\\qquad\n"
"\\sqrt{1-p}\n"
"\\begin{bmatrix}\n"
"    \\sqrt{1-\\gamma} & 0\\\\\n"
"    0 & 1\\\\\n"
"\\end{bmatrix}\\qquad\n"
"\\sqrt{1-p}\n"
"\\begin{bmatrix}\n"
"    0 & 0\\\\\n"
"    \\sqrt{\\gamma} & 0\\\\\n"
"\\end{bmatrix}\n"
"\n"
msgstr ""

#: of tensorcircuit.channels.amplitudedampingchannel:31
msgid "the damping parameter of amplitude (:math:`\\gamma`)"
msgstr ""

#: of tensorcircuit.channels.amplitudedampingchannel:33
msgid ":math:`p`"
msgstr ""

#: of tensorcircuit.channels.amplitudedampingchannel:35
msgid "An amplitude damping channel with given :math:`\\gamma` and :math:`p`"
msgstr ""

#: of tensorcircuit.channels.check_rep_transformation:1
msgid "Check the convertation between those representations."
msgstr ""

#: of tensorcircuit.channels.check_rep_transformation:3
#: tensorcircuit.channels.kraus_to_super:23
#: tensorcircuit.channels.kraus_to_super_gate:6
msgid "A sequence of Gate"
msgstr ""

#: of tensorcircuit.channels.check_rep_transformation:5
#: tensorcircuit.channels.evol_kraus:13 tensorcircuit.channels.evol_superop:11
msgid "Initial density matrix"
msgstr ""

#: of tensorcircuit.channels.check_rep_transformation:7
msgid "Whether print Kraus and new Kraus operators, defaults to False"
msgstr ""

#: of tensorcircuit.channels.choi_to_kraus:1
msgid "Convert the Choi matrix representation to Kraus operator representation."
msgstr ""

#: of tensorcircuit.channels.choi_to_kraus:3
msgid "This can be done by firstly geting eigen-decomposition of Choi-matrix"
msgstr ""

#: of tensorcircuit.channels.choi_to_kraus:5
msgid ""
"\\Lambda = \\sum_k \\gamma_k  \\vert \\phi_k \\rangle \\langle \\phi_k "
"\\vert\n"
"\n"
msgstr ""

#: of tensorcircuit.channels.choi_to_kraus:8
msgid "Then define Kraus operators"
msgstr ""

#: of tensorcircuit.channels.choi_to_kraus:10
#, python-brace-format
msgid ""
"K_k = \\sqrt{\\gamma_k} V_k\n"
"\n"
msgstr ""

#: of tensorcircuit.channels.choi_to_kraus:13
msgid ""
"where :math:`\\gamma_k\\geq0` and :math:`\\phi_k` is the col-val "
"vectorization of :math:`V_k` ."
msgstr ""

#: of tensorcircuit.channels.choi_to_kraus tensorcircuit.channels.evol_kraus
#: tensorcircuit.channels.evol_superop
#: tensorcircuit.channels.kraus_identity_check
#: tensorcircuit.channels.kraus_to_super tensorcircuit.channels.super_to_choi
msgid "Examples"
msgstr ""

#: of tensorcircuit.channels.choi_to_kraus:24
#: tensorcircuit.channels.choi_to_super:3
#: tensorcircuit.channels.kraus_to_choi:5
#: tensorcircuit.channels.super_to_choi:28
msgid "Choi matrix"
msgstr ""

#: of tensorcircuit.channels.choi_to_kraus:26
msgid ""
"A dictionary to restrict the calculation of kraus matrices. The "
"restriction can be the number of kraus terms, which is jitable. It can "
"also be the truncattion error, which is not jitable."
msgstr ""

#: of tensorcircuit.channels.choi_to_kraus:29
#: tensorcircuit.channels.krausgate_to_krausmatrix:5
#: tensorcircuit.channels.krausmatrix_to_krausgate:5
msgid "A list of Kraus operators"
msgstr ""

#: of tensorcircuit.channels.choi_to_super:1
msgid "Convert from Choi representation to Superoperator representation."
msgstr ""

#: of tensorcircuit.channels.choi_to_super:5
#: tensorcircuit.channels.evol_superop:13
#: tensorcircuit.channels.super_to_choi:26
#: tensorcircuit.channels.super_to_kraus:3
msgid "Superoperator"
msgstr ""

#: of tensorcircuit.channels.composedkraus:1
msgid "Compose the noise channels"
msgstr ""

#: of tensorcircuit.channels.composedkraus:3
msgid "One noise channel"
msgstr ""

#: of tensorcircuit.channels.composedkraus:5
msgid "Another noise channel"
msgstr ""

#: of tensorcircuit.channels.composedkraus:7
msgid "Composed nosie channel"
msgstr ""

#: of tensorcircuit.channels.depolarizingchannel:1
msgid "Return a Depolarizing Channel"
msgstr ""

#: of tensorcircuit.channels.depolarizingchannel:3
#, python-brace-format
msgid ""
"\\sqrt{1-p_x-p_y-p_z}\n"
"\\begin{bmatrix}\n"
"    1 & 0\\\\\n"
"    0 & 1\\\\\n"
"\\end{bmatrix}\\qquad\n"
"\\sqrt{p_x}\n"
"\\begin{bmatrix}\n"
"    0 & 1\\\\\n"
"    1 & 0\\\\\n"
"\\end{bmatrix}\\qquad\n"
"\\sqrt{p_y}\n"
"\\begin{bmatrix}\n"
"    0 & -1j\\\\\n"
"    1j & 0\\\\\n"
"\\end{bmatrix}\\qquad\n"
"\\sqrt{p_z}\n"
"\\begin{bmatrix}\n"
"    1 & 0\\\\\n"
"    0 & -1\\\\\n"
"\\end{bmatrix}\n"
"\n"
msgstr ""

#: of tensorcircuit.channels.depolarizingchannel:30
msgid ":math:`p_x`"
msgstr ""

#: of tensorcircuit.channels.depolarizingchannel:32
msgid ":math:`p_y`"
msgstr ""

#: of tensorcircuit.channels.depolarizingchannel:34
msgid ":math:`p_z`"
msgstr ""

#: of tensorcircuit.channels.depolarizingchannel:36
#: tensorcircuit.channels.generaldepolarizingchannel:39
#: tensorcircuit.channels.isotropicdepolarizingchannel:28
msgid "Sequences of Gates"
msgstr ""

#: of tensorcircuit.channels.evol_kraus:1
msgid "The dynamic evolution according to Kraus operators."
msgstr ""

#: of tensorcircuit.channels.evol_kraus:3
#, python-brace-format
msgid ""
"\\rho' = \\sum_{k} K_k \\rho K_k^{\\dagger}\n"
"\n"
msgstr ""

#: of tensorcircuit.channels.evol_kraus:15
#: tensorcircuit.channels.super_to_kraus:5
msgid "A list of Kraus operator"
msgstr ""

#: of tensorcircuit.channels.evol_kraus:17
#: tensorcircuit.channels.evol_superop:15
msgid "Final density matrix"
msgstr ""

#: of tensorcircuit.channels.evol_superop:1
msgid "The dynamic evolution according to Superoperator."
msgstr ""

#: of tensorcircuit.channels.generaldepolarizingchannel:1
msgid ""
"Return a depolarizing channel. If :math:`p` is a float number, the one "
"qubit channel is"
msgstr ""

#: of tensorcircuit.channels.generaldepolarizingchannel:4
#, python-brace-format
msgid "\\mathcal{E}(\\rho) = (1 - 3p)\\rho + p(X\\rho X + Y\\rho Y + Z\\rho Z)"
msgstr ""

#: of tensorcircuit.channels.generaldepolarizingchannel:8
msgid "Or alternatively"
msgstr ""

#: of tensorcircuit.channels.generaldepolarizingchannel:10
#, python-brace-format
msgid "\\mathcal{E}(\\rho) = 4p \\frac{I}{2} + (1 - 4p) \\rho"
msgstr ""

#: of tensorcircuit.channels.generaldepolarizingchannel:16
msgid ""
"The definition of ``p`` in this method is different from "
":func:`isotropicdepolarizingchannel`."
msgstr ""

#: of tensorcircuit.channels.generaldepolarizingchannel:19
msgid "And if :math:`p` is a sequence, the one qubit channel is"
msgstr ""

#: of tensorcircuit.channels.generaldepolarizingchannel:21
#, python-brace-format
msgid ""
"\\mathcal{E}(\\rho) = (1 - \\sum_i p_i) \\rho + p_1 X\\rho X + p_2 Y\\rho"
" Y + p_3 \\rho Z"
msgstr ""

#: of tensorcircuit.channels.generaldepolarizingchannel:25
msgid "The logic for two-qubit or more-qubit channel follows similarly."
msgstr ""

#: of tensorcircuit.channels.generaldepolarizingchannel:35
msgid "parameter for each Pauli channel"
msgstr ""

#: of tensorcircuit.channels.generaldepolarizingchannel:37
#: tensorcircuit.channels.isotropicdepolarizingchannel:26
msgid "number of qubits, defaults 1"
msgstr ""

#: of tensorcircuit.channels.is_hermitian_matrix:1
msgid "Test if an array is a Hermitian matrix"
msgstr ""

#: of tensorcircuit.channels.is_hermitian_matrix:3
msgid "Matrix"
msgstr ""

#: of tensorcircuit.channels.is_hermitian_matrix:5
msgid "_description_, defaults to 1e-8"
msgstr ""

#: of tensorcircuit.channels.is_hermitian_matrix:7
msgid "_description_, defaults to 1e-5"
msgstr ""

#: of tensorcircuit.channels.isotropicdepolarizingchannel:1
msgid "Return an isotropic depolarizing channel."
msgstr ""

#: of tensorcircuit.channels.isotropicdepolarizingchannel:3
#, python-brace-format
msgid "\\mathcal{E}(\\rho) = (1 - p)\\rho + p/(4^n-1)\\sum_j P_j \\rho P_j"
msgstr ""

#: of tensorcircuit.channels.isotropicdepolarizingchannel:7
msgid ""
"where $n$ is the number of qubits and $P_j$ are $n$-qubit Pauli strings "
"except $I$. Or alternatively"
msgstr ""

#: of tensorcircuit.channels.isotropicdepolarizingchannel:10
#, python-brace-format
msgid ""
"\\mathcal{E}(\\rho) = \\frac{4^n}{4^n-1}p \\frac{I}{2} + (1 - "
"\\frac{4^n}{4^n-1}p) \\rho"
msgstr ""

#: of tensorcircuit.channels.isotropicdepolarizingchannel:16
msgid ""
"The definition of ``p`` in this method is different from "
":func:`generaldepolarizingchannel`."
msgstr ""

#: of tensorcircuit.channels.isotropicdepolarizingchannel:24
msgid "error probability"
msgstr ""

#: of tensorcircuit.channels.kraus_identity_check:1
msgid "Check identity of Kraus operators."
msgstr ""

#: of tensorcircuit.channels.kraus_identity_check:3
#, python-brace-format
msgid ""
"\\sum_{k}^{} K_k^{\\dagger} K_k = I\n"
"\n"
msgstr ""

#: of tensorcircuit.channels.kraus_identity_check:12
msgid "List of Kraus operators."
msgstr ""

#: of tensorcircuit.channels.kraus_to_choi:1
msgid "Convert from Kraus representation to Choi representation."
msgstr ""

#: of tensorcircuit.channels.kraus_to_choi:3
msgid "A list Kraus operators"
msgstr ""

#: of tensorcircuit.channels.kraus_to_super:1
msgid ""
"Convert Kraus operator representation to Louivile-Superoperator "
"representation."
msgstr ""

#: of tensorcircuit.channels.kraus_to_super:3
msgid ""
"In the col-vec basis, the evolution of a state :math:`\\rho` in terms of "
"tensor components of superoperator :math:`\\varepsilon` can be expressed "
"as"
msgstr ""

#: of tensorcircuit.channels.kraus_to_super:6
#, python-brace-format
msgid ""
"\\rho'_{mn} = \\sum_{\\mu \\nu}^{} \\varepsilon_{nm,\\nu \\mu} "
"\\rho_{\\mu \\nu}\n"
"\n"
msgstr ""

#: of tensorcircuit.channels.kraus_to_super:9
msgid ""
"The superoperator :math:`\\varepsilon` must make the dynamic map from "
":math:`\\rho` to :math:`\\rho'` to satisfy hermitian-perserving (HP), "
"trace-preserving (TP), and completely positive (CP)."
msgstr ""

#: of tensorcircuit.channels.kraus_to_super:12
msgid "We can construct the superoperator from Kraus operators by"
msgstr ""

#: of tensorcircuit.channels.kraus_to_super:14
#, python-brace-format
msgid ""
"\\varepsilon = \\sum_{k} K_k^{*} \\otimes K_k\n"
"\n"
msgstr ""

#: of tensorcircuit.channels.kraus_to_super:25
msgid "The corresponding Tensor of Superoperator"
msgstr ""

#: of tensorcircuit.channels.kraus_to_super_gate:1
msgid "Convert Kraus operators to one Tensor (as one Super Gate)."
msgstr ""

#: of tensorcircuit.channels.kraus_to_super_gate:3
#, python-brace-format
msgid ""
"\\sum_{k}^{} K_k \\otimes K_k^{*}\n"
"\n"
msgstr ""

#: of tensorcircuit.channels.kraus_to_super_gate:8
msgid "The corresponding Tensor of the list of Kraus operators"
msgstr ""

#: of tensorcircuit.channels.krausgate_to_krausmatrix:1
msgid "Convert Kraus of Gate form to Matrix form."
msgstr ""

#: of tensorcircuit.channels.krausgate_to_krausmatrix:3
#: tensorcircuit.channels.krausmatrix_to_krausgate:3
msgid "A list of Kraus"
msgstr ""

#: of tensorcircuit.channels.krausmatrix_to_krausgate:1
msgid "Convert Kraus of Matrix form to Gate form."
msgstr ""

#: of tensorcircuit.channels.phasedampingchannel:1
msgid "Return a phase damping channel with given :math:`\\gamma`"
msgstr ""

#: of tensorcircuit.channels.phasedampingchannel:3
#, python-brace-format
msgid ""
"\\begin{bmatrix}\n"
"    1 & 0\\\\\n"
"    0 & \\sqrt{1-\\gamma}\\\\\n"
"\\end{bmatrix}\\qquad\n"
"\\begin{bmatrix}\n"
"    0 & 0\\\\\n"
"    0 & \\sqrt{\\gamma}\\\\\n"
"\\end{bmatrix}\n"
"\n"
msgstr ""

#: of tensorcircuit.channels.phasedampingchannel:18
msgid "The damping parameter of phase (:math:`\\gamma`)"
msgstr ""

#: of tensorcircuit.channels.phasedampingchannel:20
msgid "A phase damping channel with given :math:`\\gamma`"
msgstr ""

#: of tensorcircuit.channels.resetchannel:1
#: tensorcircuit.channels.resetchannel:18
msgid "Reset channel"
msgstr ""

#: of tensorcircuit.channels.resetchannel:3
#, python-brace-format
msgid ""
"\\begin{bmatrix}\n"
"    1 & 0\\\\\n"
"    0 & 0\\\\\n"
"\\end{bmatrix}\\qquad\n"
"\\begin{bmatrix}\n"
"    0 & 1\\\\\n"
"    0 & 0\\\\\n"
"\\end{bmatrix}\n"
"\n"
msgstr ""

#: of tensorcircuit.channels.reshuffle:1
msgid "Reshuffle the dimension index of a matrix."
msgstr ""

#: of tensorcircuit.channels.reshuffle:3
msgid "Input matrix"
msgstr ""

#: of tensorcircuit.channels.reshuffle:5
msgid "required order"
msgstr ""

#: of tensorcircuit.channels.reshuffle:7
msgid "Reshuffled matrix"
msgstr ""

#: of tensorcircuit.channels.super_to_choi:1
msgid "Convert Louivile-Superoperator representation to Choi representation."
msgstr ""

#: of tensorcircuit.channels.super_to_choi:3
msgid ""
"In the col-vec basis, the evolution of a state :math:`\\rho` in terms of "
"Choi matrix :math:`\\Lambda` can be expressed as"
msgstr ""

#: of tensorcircuit.channels.super_to_choi:6
#, python-brace-format
msgid ""
"\\rho'_{mn} = \\sum_{\\mu,\\nu}^{} \\Lambda_{\\mu m,\\nu n} \\rho_{\\mu "
"\\nu}\n"
"\n"
msgstr ""

#: of tensorcircuit.channels.super_to_choi:9
msgid ""
"The Choi matrix :math:`\\Lambda` must make the dynamic map from "
":math:`\\rho` to :math:`\\rho'` to satisfy hermitian-perserving (HP), "
"trace-preserving (TP), and completely positive (CP)."
msgstr ""

#: of tensorcircuit.channels.super_to_choi:12
msgid ""
"Interms of tensor components we have the relationship of Louivile-"
"Superoperator representation and Choi representation"
msgstr ""

#: of tensorcircuit.channels.super_to_choi:15
#, python-brace-format
msgid ""
"\\Lambda_{mn,\\mu \\nu} = \\varepsilon_{\\nu n,\\mu m}\n"
"\n"
msgstr ""

#: of tensorcircuit.channels.super_to_kraus:1
msgid "Convert from Superoperator representation to Kraus representation."
msgstr ""

#: of tensorcircuit.channels.thermalrelaxationchannel:1
msgid "Return a thermal_relaxation_channel"
msgstr ""

#: of tensorcircuit.channels.thermalrelaxationchannel:9
msgid "the T1 relaxation time."
msgstr ""

#: of tensorcircuit.channels.thermalrelaxationchannel:11
msgid "the T2 dephasing time."
msgstr ""

#: of tensorcircuit.channels.thermalrelaxationchannel:13
msgid "gate time"
msgstr ""

#: of tensorcircuit.channels.thermalrelaxationchannel:15
msgid ""
"method to express error (default: \"ByChoi\"). When :math:`T1>T2`, choose"
" method \"ByKraus\" or \"ByChoi\" for jit. When :math:`T1<T2`,choose "
"method \"ByChoi\" for jit. Users can also set method as \"AUTO\" and "
"never mind the relative magnitude of :math:`T1,T2`, which is not jitable."
msgstr ""

#: of tensorcircuit.channels.thermalrelaxationchannel:19
msgid "the population of  state :math:`|1\\rangle` at equilibrium (default: 0)"
msgstr ""

#: of tensorcircuit.channels.thermalrelaxationchannel:21
msgid "A thermal_relaxation_channel"
msgstr ""

#: ../../source/api/circuit.rst:2
msgid "tensorcircuit.circuit"
msgstr ""

#: of tensorcircuit.circuit:1
msgid ""
"Quantum circuit: the state simulator. Supports qubit (dim=2) and qudit (3"
" <= dim <= 36) systems. For string-encoded samples/counts, digits use "
"0-9A-Z where A=10, ..., Z=35."
msgstr ""

#: of tensorcircuit.circuit.Circuit:1
msgid "``Circuit`` class. Simple usage demo below."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **ANY** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.any_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:4
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.densitymatrix.DMCircuit.apply_general_kraus_delayed.<locals>.apply:4
#: tensorcircuit.densitymatrix.DMCircuit2.apply_general_kraus_delayed.<locals>.apply:4
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:4
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:5
msgid "Qubit number that the gate applies on."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:6
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:7
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:6
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:7
msgid "Parameters for the gate."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply cmz gate on the circuit using hyperedge support for digonal gates. "
"See :py:meth:`tensorcircuit.gates.cmz_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **CNOT** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.cnot_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\"
"    0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j & "
"1.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
msgid "Qubit number that the gate applies on. The matrix for the gate is"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid ""
"\\begin{bmatrix}    1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & "
"1.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j\\\\    "
"0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **CPHASE** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.cphase_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **CR** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.cr_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **CRX** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.crx_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **CRY** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.cry_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **CRZ** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.crz_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **CU** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.cu_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **CY** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.cy_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\"
"    0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j & "
"0.-1.j\\\\    0.+0.j & 0.+0.j & 0.+1.j & 0.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid ""
"\\begin{bmatrix}    1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & "
"1.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j & 0.-1.j\\\\    "
"0.+0.j & 0.+0.j & 0.+1.j & 0.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **CZ** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.cz_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\"
"    0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j & "
"0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j & -1.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid ""
"\\begin{bmatrix}    1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & "
"1.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j\\\\    "
"0.+0.j & 0.+0.j & 0.+0.j & -1.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply diagonal gate on the circuit using hyperedge support for digonal "
"gates. See :py:meth:`tensorcircuit.gates.diagonal_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **EXP** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.exp_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **EXP1** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.exp1_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **FREDKIN** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.fredkin_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & "
"0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 1.+0.j & 0.+0.j & "
"0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & "
"1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & "
"0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    "
"0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\"
"    0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & "
"0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & "
"0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & "
"0.+0.j & 0.+0.j & 1.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid ""
"\\begin{bmatrix}    1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j &"
" 0.+0.j & 0.+0.j\\\\    0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & "
"0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & "
"0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j & "
"1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & "
"0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & "
"0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j\\\\    "
"0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\\\"
"    0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j"
" \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **H** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.h_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    0.70710677+0.j & 0.70710677+0.j\\\\    "
"0.70710677+0.j & -0.70710677+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid ""
"\\begin{bmatrix}    0.70710677+0.j & 0.70710677+0.j\\\\    0.70710677+0.j"
" & -0.70710677+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **I** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.i_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    1.+0.j & 0.+0.j\\\\    0.+0.j & 1.+0.j "
"\\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid "\\begin{bmatrix}    1.+0.j & 0.+0.j\\\\    0.+0.j & 1.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **ISWAP** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.iswap_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply mpo gate in MPO format on the circuit. See "
":py:meth:`tensorcircuit.gates.mpo_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply multicontrol gate in MPO format on the circuit. See "
":py:meth:`tensorcircuit.gates.multicontrol_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **ORX** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.orx_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **ORY** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.ory_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **ORZ** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.orz_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **OX** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.ox_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\\\"
"    1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j & "
"0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid ""
"\\begin{bmatrix}    0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\\\    1.+0.j & "
"0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j\\\\    "
"0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **OY** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.oy_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    0.+0.j & 0.-1.j & 0.+0.j & 0.+0.j\\\\"
"    0.+1.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j & "
"0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid ""
"\\begin{bmatrix}    0.+0.j & 0.-1.j & 0.+0.j & 0.+0.j\\\\    0.+1.j & "
"0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j\\\\    "
"0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **OZ** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.oz_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\"
"    0.+0.j & -1.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j & "
"0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid ""
"\\begin{bmatrix}    1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & "
"-1.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j\\\\"
"    0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **PHASE** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.phase_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **R** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.r_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **RX** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.rx_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **RXX** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.rxx_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **RY** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.ry_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **RYY** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.ryy_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **RZ** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.rz_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply rzm gate on the circuit using hyperedge support for digonal gates. "
"See :py:meth:`tensorcircuit.gates.rzm_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **RZZ** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.rzz_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **S** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.s_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    1.+0.j & 0.+0.j\\\\    0.+0.j & 0.+1.j "
"\\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid "\\begin{bmatrix}    1.+0.j & 0.+0.j\\\\    0.+0.j & 0.+1.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **SD** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.sd_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    1.+0.j & 0.+0.j\\\\    0.+0.j & 0.-1.j "
"\\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid "\\begin{bmatrix}    1.+0.j & 0.+0.j\\\\    0.+0.j & 0.-1.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **SU4** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.su4_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **SWAP** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.swap_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\"
"    0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j\\\\    0.+0.j & 1.+0.j & 0.+0.j & "
"0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid ""
"\\begin{bmatrix}    1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & "
"0.+0.j & 1.+0.j & 0.+0.j\\\\    0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\\\    "
"0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **T** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.t_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    1. & +0.j & 0. & +0.j\\\\    0. & +0.j "
"& 0.70710677+0.70710677j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid ""
"\\begin{bmatrix}    1. & +0.j & 0. & +0.j\\\\    0. & +0.j & "
"0.70710677+0.70710677j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **TD** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.td_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    1. & +0.j & 0. & +0.j\\\\    0. & +0.j "
"& 0.70710677-0.70710677j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid ""
"\\begin{bmatrix}    1. & +0.j & 0. & +0.j\\\\    0. & +0.j & "
"0.70710677-0.70710677j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **TOFFOLI** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.toffoli_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & "
"0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 1.+0.j & 0.+0.j & "
"0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & "
"1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & "
"0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    "
"0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\"
"    0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & "
"0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & "
"0.+0.j & 1.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & "
"0.+0.j & 1.+0.j & 0.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid ""
"\\begin{bmatrix}    1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j &"
" 0.+0.j & 0.+0.j\\\\    0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & "
"0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & "
"0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j & "
"1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j & "
"0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j & "
"0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\\\    "
"0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j\\\\"
"    0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j"
" \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **U** gate with parameters on the circuit. See "
":py:meth:`tensorcircuit.gates.u_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **WROOT** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.wroot_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    0.70710677+0.j & -0.5 & -0.5j\\\\    "
"0.5 & -0.5j & 0.70710677+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid ""
"\\begin{bmatrix}    0.70710677+0.j & -0.5 & -0.5j\\\\    0.5 & -0.5j & "
"0.70710677+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **X** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.x_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    0.+0.j & 1.+0.j\\\\    1.+0.j & 0.+0.j "
"\\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid "\\begin{bmatrix}    0.+0.j & 1.+0.j\\\\    1.+0.j & 0.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **Y** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.y_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    0.+0.j & 0.-1.j\\\\    0.+1.j & 0.+0.j "
"\\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid "\\begin{bmatrix}    0.+0.j & 0.-1.j\\\\    0.+1.j & 0.+0.j \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:1
msgid ""
"Apply **Z** gate on the circuit. See "
":py:meth:`tensorcircuit.gates.z_gate`."
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:5
#, python-brace-format
msgid ""
"Qubit number that the gate applies on. The matrix for the gate is  .. "
"math::        \\begin{bmatrix}    1.+0.j & 0.+0.j\\\\    0.+0.j & -1.+0.j"
" \\end{bmatrix}"
msgstr ""

#: of
#: tensorcircuit.abstractcircuit.AbstractCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed.<locals>.apply_list:8
#, python-brace-format
msgid "\\begin{bmatrix}    1.+0.j & 0.+0.j\\\\    0.+0.j & -1.+0.j \\end{bmatrix}"
msgstr ""

#: of tensorcircuit.circuit.Circuit.__init__:1
msgid ""
"Circuit object based on state simulator. Do not use this class with d!=2 "
"directly, use tc.QuditCircuit instead for qudit systems."
msgstr ""

#: of tensorcircuit.circuit.Circuit.__init__:8
msgid ""
"If not None, the initial state of the circuit is taken as ``inputs`` "
"instead of :math:`\\vert 0 \\rangle^n` qubits, defaults to None."
msgstr ""

#: of tensorcircuit.circuit.Circuit.__init__:13
msgid ""
"Sequence of tensors for a MPS like initial wavefunction. The order of "
"legs for each tensor is assumed to be (bond-left, physical, bond-right)."
msgstr ""

#: of
#: tensorcircuit.circuit.Circuit.apply_general_kraus_delayed.<locals>.apply:1
#: tensorcircuit.densitymatrix.DMCircuit.apply_general_kraus_delayed.<locals>.apply:1
#: tensorcircuit.densitymatrix.DMCircuit2.apply_general_kraus_delayed.<locals>.apply:1
msgid ""
"Apply amplitudedamping quantum channel on the circuit. See "
":py:meth:`tensorcircuit.channels.amplitudedampingchannel`"
msgstr ""

#: of
#: tensorcircuit.circuit.Circuit.apply_general_kraus_delayed.<locals>.apply:4
msgid "Site index that the gate applies on."
msgstr ""

#: of
#: tensorcircuit.circuit.Circuit.apply_general_kraus_delayed.<locals>.apply:6
msgid "uniform external random number between 0 and 1"
msgstr ""

#: of
#: tensorcircuit.circuit.Circuit.apply_general_kraus_delayed.<locals>.apply:8
#: tensorcircuit.densitymatrix.DMCircuit.apply_general_kraus_delayed.<locals>.apply:6
#: tensorcircuit.densitymatrix.DMCircuit2.apply_general_kraus_delayed.<locals>.apply:6
msgid "Parameters for the channel."
msgstr ""

#: of tensorcircuit.circuit.Circuit.general_kraus:1
#: tensorcircuit.quditcircuit.QuditCircuit.general_kraus:1
msgid ""
"Monte Carlo trajectory simulation of general Kraus channel whose Kraus "
"operators cannot be amplified to unitary operators. For unitary operators"
" composed Kraus channel, :py:meth:`unitary_kraus` is much faster."
msgstr ""

#: of tensorcircuit.circuit.Circuit.general_kraus:5
#: tensorcircuit.quditcircuit.QuditCircuit.general_kraus:5
msgid ""
"This function is jittable in theory. But only jax+GPU combination is "
"recommended for jit since the graph building time is too long for other "
"backend options; though the running time of the function is very fast for"
" every case."
msgstr ""

#: of tensorcircuit.circuit.Circuit.general_kraus:9
#: tensorcircuit.quditcircuit.QuditCircuit.general_kraus:9
msgid "A list of ``tn.Node`` for Kraus operators."
msgstr ""

#: of tensorcircuit.circuit.Circuit.general_kraus:11
#: tensorcircuit.quditcircuit.QuditCircuit.general_kraus:11
msgid "The qubits index that Kraus channel is applied on."
msgstr ""

#: of tensorcircuit.circuit.Circuit.general_kraus:13
#: tensorcircuit.quditcircuit.QuditCircuit.general_kraus:13
msgid ""
"Random tensor uniformly between 0 or 1, defaults to be None, when the "
"random number will be generated automatically"
msgstr ""

#: of
#: tensorcircuit.circuit.Circuit.apply_general_kraus_delayed.<locals>.apply:1
#: tensorcircuit.densitymatrix.DMCircuit.apply_general_kraus_delayed.<locals>.apply:1
#: tensorcircuit.densitymatrix.DMCircuit2.apply_general_kraus_delayed.<locals>.apply:1
msgid ""
"Apply depolarizing quantum channel on the circuit. See "
":py:meth:`tensorcircuit.channels.depolarizingchannel`"
msgstr ""

#: of tensorcircuit.circuit.Circuit.depolarizing2:1
msgid ""
"Apply a depolarizing channel to the circuit in a Monte Carlo way. For "
"each call, one of the Pauli gates (X, Y, Z) or an Identity gate is "
"applied to the qubit at the given index based on the probabilities `px`, "
"`py`, and `pz`."
msgstr ""

#: of tensorcircuit.circuit.Circuit.depolarizing2:5
msgid "The index of the qubit to apply the depolarizing channel on."
msgstr ""

#: of tensorcircuit.circuit.Circuit.depolarizing2:7
msgid "The probability of applying an X gate."
msgstr ""

#: of tensorcircuit.circuit.Circuit.depolarizing2:9
msgid "The probability of applying a Y gate."
msgstr ""

#: of tensorcircuit.circuit.Circuit.depolarizing2:11
msgid "The probability of applying a Z gate."
msgstr ""

#: of tensorcircuit.circuit.Circuit.depolarizing2:13
msgid ""
"A random number between 0 and 1 to determine which gate to apply. If "
"None, a random number is generated automatically. Defaults to None."
msgstr ""

#: of tensorcircuit.circuit.Circuit.depolarizing2:16
msgid "Returns 0.0. The function modifies the circuit in place."
msgstr ""

#: of tensorcircuit.circuit.Circuit.depolarizing_reference:1
msgid ""
"Apply depolarizing channel in a Monte Carlo way, i.e. for each call of "
"this method, one of gates from X, Y, Z, I are applied on the circuit "
"based on the probability indicated by ``px``, ``py``, ``pz``."
msgstr ""

#: of tensorcircuit.circuit.Circuit.depolarizing_reference:6
msgid "The qubit that depolarizing channel is on"
msgstr ""

#: of tensorcircuit.circuit.Circuit.depolarizing_reference:8
msgid "probability for X noise"
msgstr ""

#: of tensorcircuit.circuit.Circuit.depolarizing_reference:10
msgid "probability for Y noise"
msgstr ""

#: of tensorcircuit.circuit.Circuit.depolarizing_reference:12
msgid "probability for Z noise"
msgstr ""

#: of tensorcircuit.circuit.Circuit.depolarizing_reference:14
msgid "random seed uniformly from 0 to 1, defaults to None (generated implicitly)"
msgstr ""

#: of tensorcircuit.circuit.Circuit.depolarizing_reference:16
msgid "int Tensor, the element lookup: [0: x, 1: y, 2: z, 3: I]"
msgstr ""

#: of tensorcircuit.circuit.Circuit.expectation:1
msgid ""
"Compute the expectation of corresponding operators. For qudit (d > 2), "
"ensure that operator tensor shapes are consistent with d (each site "
"contributes two axes of size d)."
msgstr ""

#: of tensorcircuit.circuit.Circuit.expectation:5
msgid ""
"Noise shorthand (via noise_conf) is qubit-only; for d>2, use explicit "
"operators."
msgstr ""

#: of tensorcircuit.circuit.Circuit.expectation:28
#: tensorcircuit.densitymatrix.DMCircuit.expectation:3
msgid ""
"Operator and its position on the circuit, eg. ``(tc.gates.z(), [1, ]), "
"(tc.gates.x(), [2, ])`` is for operator :math:`Z_1X_2`."
msgstr ""

#: of tensorcircuit.circuit.Circuit.expectation:40
msgid ""
"external randomness given by tensor uniformly from [0, 1], defaults to "
"None, used for noisy circuit sampling"
msgstr ""

#: of tensorcircuit.circuit.Circuit.expectation:43
#: tensorcircuit.circuit.expectation:53
#: tensorcircuit.quditcircuit.QuditCircuit.expectation:12
msgid "\"Cannot measure two operators in one index\""
msgstr ""

#: of
#: tensorcircuit.circuit.Circuit.apply_general_kraus_delayed.<locals>.apply:1
#: tensorcircuit.densitymatrix.DMCircuit.apply_general_kraus_delayed.<locals>.apply:1
#: tensorcircuit.densitymatrix.DMCircuit2.apply_general_kraus_delayed.<locals>.apply:1
msgid ""
"Apply generaldepolarizing quantum channel on the circuit. See "
":py:meth:`tensorcircuit.channels.generaldepolarizingchannel`"
msgstr ""

#: of tensorcircuit.circuit.Circuit.get_quoperator:1
#: tensorcircuit.quditcircuit.QuditCircuit.get_quoperator:1
msgid ""
"Get the ``QuOperator`` MPO like representation of the circuit unitary "
"without contraction."
msgstr ""

#: of tensorcircuit.circuit.Circuit.get_quoperator:3
#: tensorcircuit.quditcircuit.QuditCircuit.get_quoperator:3
msgid ""
"``QuOperator`` object for the circuit unitary (open indices for the input"
" state)"
msgstr ""

#: of tensorcircuit.circuit.Circuit.is_valid:1
msgid "[WIP], check whether the circuit is legal."
msgstr ""

#: of tensorcircuit.circuit.Circuit.is_valid:3
msgid "The bool indicating whether the circuit is legal"
msgstr ""

#: of
#: tensorcircuit.circuit.Circuit.apply_general_kraus_delayed.<locals>.apply:1
#: tensorcircuit.densitymatrix.DMCircuit.apply_general_kraus_delayed.<locals>.apply:1
#: tensorcircuit.densitymatrix.DMCircuit2.apply_general_kraus_delayed.<locals>.apply:1
msgid ""
"Apply isotropicdepolarizing quantum channel on the circuit. See "
":py:meth:`tensorcircuit.channels.isotropicdepolarizingchannel`"
msgstr ""

#: of tensorcircuit.circuit.Circuit.matrix:1
#: tensorcircuit.quditcircuit.QuditCircuit.matrix:1
msgid ""
"Get the unitary matrix for the circuit irrespective with the circuit "
"input state."
msgstr ""

#: of tensorcircuit.circuit.Circuit.matrix:3
#: tensorcircuit.quditcircuit.QuditCircuit.matrix:3
msgid "The circuit unitary matrix"
msgstr ""

#: of tensorcircuit.circuit.Circuit.measure_reference:1
msgid "Take measurement on the given quantum lines by ``index``."
msgstr ""

#: of tensorcircuit.circuit.Circuit.measure_reference:3
msgid ""
"Return format: - For d <= 36, the sample is a base-d string using 0-9A-Z "
"(A=10,...)."
msgstr ""

#: of tensorcircuit.circuit.Circuit.measure_reference:19
#: tensorcircuit.mpscircuit.MPSCircuit.measure:3
msgid "Measure on which quantum line."
msgstr ""

#: of tensorcircuit.circuit.Circuit.mid_measurement:1
msgid ""
"Middle measurement in z-basis on the circuit, note the wavefunction "
"output is not normalized with ``mid_measurement`` involved, one should "
"normalize the state manually if needed. This is a post-selection method "
"as keep is provided as a prior."
msgstr ""

#: of tensorcircuit.circuit.Circuit.mid_measurement:5
msgid "The index of qubit that the Z direction postselection applied on."
msgstr ""

#: of tensorcircuit.circuit.Circuit.mid_measurement:7
#, python-brace-format
msgid "the post-selected digit in {0, ..., d-1}, defaults to be 0."
msgstr ""

#: of
#: tensorcircuit.circuit.Circuit.apply_general_kraus_delayed.<locals>.apply:1
#: tensorcircuit.densitymatrix.DMCircuit.apply_general_kraus_delayed.<locals>.apply:1
#: tensorcircuit.densitymatrix.DMCircuit2.apply_general_kraus_delayed.<locals>.apply:1
msgid ""
"Apply phasedamping quantum channel on the circuit. See "
":py:meth:`tensorcircuit.channels.phasedampingchannel`"
msgstr ""

#: of tensorcircuit.circuit.Circuit.replace_mps_inputs:1
msgid ""
"Replace the input state in MPS representation while keep the circuit "
"structure unchanged."
msgstr ""

#: of tensorcircuit.circuit.Circuit.replace_mps_inputs:18
#: tensorcircuit.quditcircuit.QuditCircuit.replace_mps_inputs:15
msgid "(Nodes, dangling Edges) for a MPS like initial wavefunction."
msgstr ""

#: of
#: tensorcircuit.circuit.Circuit.apply_general_kraus_delayed.<locals>.apply:1
#: tensorcircuit.densitymatrix.DMCircuit.apply_general_kraus_delayed.<locals>.apply:1
#: tensorcircuit.densitymatrix.DMCircuit2.apply_general_kraus_delayed.<locals>.apply:1
msgid ""
"Apply reset quantum channel on the circuit. See "
":py:meth:`tensorcircuit.channels.resetchannel`"
msgstr ""

#: of tensorcircuit.circuit.Circuit.wavefunction:1
#: tensorcircuit.mpscircuit.MPSCircuit.wavefunction:1
#: tensorcircuit.quditcircuit.QuditCircuit.wavefunction:1
msgid "Compute the output wavefunction from the circuit."
msgstr ""

#: of tensorcircuit.circuit.Circuit.wavefunction:3
#: tensorcircuit.quditcircuit.QuditCircuit.wavefunction:3
msgid ""
"The str indicating the form of the output wavefunction. \"default\": "
"[-1], \"ket\": [-1, 1], \"bra\": [1, -1]"
msgstr ""

#: of tensorcircuit.circuit.Circuit.wavefunction:6
#: tensorcircuit.quditcircuit.QuditCircuit.wavefunction:6
msgid "Tensor with the corresponding shape."
msgstr ""

#: of
#: tensorcircuit.circuit.Circuit.apply_general_kraus_delayed.<locals>.apply:1
#: tensorcircuit.densitymatrix.DMCircuit.apply_general_kraus_delayed.<locals>.apply:1
#: tensorcircuit.densitymatrix.DMCircuit2.apply_general_kraus_delayed.<locals>.apply:1
msgid ""
"Apply thermalrelaxation quantum channel on the circuit. See "
":py:meth:`tensorcircuit.channels.thermalrelaxationchannel`"
msgstr ""

#: of tensorcircuit.circuit.Circuit.unitary_kraus:1
#: tensorcircuit.quditcircuit.QuditCircuit.unitary_kraus:1
msgid ""
"Apply unitary gates in ``kraus`` randomly based on corresponding "
"``prob``. If ``prob`` is ``None``, this is reduced to kraus channel "
"language."
msgstr ""

#: of tensorcircuit.circuit.Circuit.unitary_kraus:4
#: tensorcircuit.quditcircuit.QuditCircuit.unitary_kraus:4
msgid "List of ``tc.gates.Gate`` or just Tensors"
msgstr ""

#: of tensorcircuit.circuit.Circuit.unitary_kraus:6
#: tensorcircuit.quditcircuit.QuditCircuit.unitary_kraus:6
msgid "prob list with the same size as ``kraus``, defaults to None"
msgstr ""

#: of tensorcircuit.circuit.Circuit.unitary_kraus:8
#: tensorcircuit.quditcircuit.QuditCircuit.unitary_kraus:8
msgid "random seed between 0 to 1, defaults to None"
msgstr ""

#: of tensorcircuit.circuit.Circuit.unitary_kraus:10
#: tensorcircuit.quditcircuit.QuditCircuit.unitary_kraus:10
msgid "shape [] int dtype tensor indicates which kraus gate is actually applied"
msgstr ""

#: of tensorcircuit.circuit.Circuit.unitary_kraus2:1
msgid ""
"Apply a unitary Kraus channel to the circuit using a Monte Carlo "
"approach. This method is functionally similar to `unitary_kraus` but uses"
" `backend.switch` for selecting the Kraus operator, which can have "
"different performance characteristics on some backends."
msgstr ""

#: of tensorcircuit.circuit.Circuit.unitary_kraus2:5
msgid ""
"A random Kraus operator from the provided list is applied to the circuit "
"based on the given probabilities. This method is jittable and suitable "
"for simulating noisy quantum circuits where the noise is represented by "
"unitary Kraus operators."
msgstr ""

#: of tensorcircuit.circuit.Circuit.unitary_kraus2:10
msgid ""
"This method may have issues with `vmap` due to potential concurrent "
"access locks, potentially related with `backend.switch`. `unitary_kraus` "
"is generally recommended."
msgstr ""

#: of tensorcircuit.circuit.Circuit.unitary_kraus2:13
msgid "A sequence of `Gate` objects representing the unitary Kraus operators."
msgstr ""

#: of tensorcircuit.circuit.Circuit.unitary_kraus2:15
msgid "The qubit indices on which to apply the Kraus channel."
msgstr ""

#: of tensorcircuit.circuit.Circuit.unitary_kraus2:17
msgid ""
"A sequence of probabilities corresponding to each Kraus operator. If "
"None, probabilities are derived from the operators themselves. Defaults "
"to None."
msgstr ""

#: of tensorcircuit.circuit.Circuit.unitary_kraus2:20
msgid ""
"A random number between 0 and 1 to determine which Kraus operator to "
"apply. If None, a random number is generated automatically. Defaults to "
"None."
msgstr ""

#: of tensorcircuit.circuit.Circuit.unitary_kraus2:23
msgid "An optional name for the operation. Defaults to None."
msgstr ""

#: of tensorcircuit.circuit.Circuit.unitary_kraus2:25
msgid "A tensor indicating which Kraus operator was applied."
msgstr ""

#: of tensorcircuit.circuit.expectation:1
msgid ""
"Compute :math:`\\langle bra\\vert ops \\vert ket\\rangle`. For qudit "
"systems (d>2), ops must be reshaped with per-site axes of length d."
msgstr ""

#: of tensorcircuit.circuit.expectation:4
msgid "Example 1 (:math:`bra` is same as :math:`ket`)"
msgstr ""

#: of tensorcircuit.circuit.expectation:25
msgid "Example 2 (:math:`bra` is different from :math:`ket`)"
msgstr ""

#: of tensorcircuit.circuit.expectation:43
msgid ":math:`ket`. The state in tensor or ``QuVector`` format"
msgstr ""

#: of tensorcircuit.circuit.expectation:45
msgid ":math:`bra`, defaults to None, which is the same as ``ket``."
msgstr ""

#: of tensorcircuit.circuit.expectation:47
msgid "dimension of the circuit (defaults to 2)"
msgstr ""

#: of tensorcircuit.circuit.expectation:49
msgid ""
":math:`bra` changes to the adjoint matrix of :math:`bra`, defaults to "
"True."
msgstr ""

#: of tensorcircuit.circuit.expectation:51
msgid "Normalize the :math:`ket` and :math:`bra`, defaults to False."
msgstr ""

#: of tensorcircuit.circuit.expectation:54
msgid "The result of :math:`\\langle bra\\vert ops \\vert ket\\rangle`."
msgstr ""

#: ../../source/api/cloud.rst:2
msgid "tensorcircuit.cloud"
msgstr ""

#: ../../source/api/cloud/abstraction.rst:2
msgid "tensorcircuit.cloud.abstraction"
msgstr ""

#: of tensorcircuit.cloud.abstraction:1
msgid "Abstraction for Provider, Device and Task"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Device:1
msgid "Device abstraction for cloud connection, eg. quantum chips"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Device.list_properties:1
msgid "List all device properties in as dict"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Device.native_gates:1
msgid "List native gates supported for the device, str conforms qiskit convention"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Device.topology:1
msgid "Get the bidirectional topology link list of the device"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Device.topology_graph:1
msgid "Get the qubit topology in ``nx.Graph`` or directly visualize it"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Device.topology_graph:3
#: tensorcircuit.vis.render_pdf:23
msgid "[description], defaults to False"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Provider:1
msgid "Provider abstraction for cloud connection, eg. \"tencent\", \"local\""
msgstr ""

#: of tensorcircuit.cloud.abstraction.TCException.add_note:1
#: tensorcircuit.cloud.abstraction.TaskException.add_note:1
#: tensorcircuit.cloud.abstraction.TaskFailed.add_note:1
#: tensorcircuit.cloud.abstraction.TaskUnfinished.add_note:1
#: tensorcircuit.cloud.utils.HttpStatusError.add_note:1
#: tensorcircuit.cons.NodesReturn.add_note:1
msgid "Exception.add_note(note) -- add a note to the exception"
msgstr ""

#: of tensorcircuit.cloud.abstraction.TCException.with_traceback:1
#: tensorcircuit.cloud.abstraction.TaskException.with_traceback:1
#: tensorcircuit.cloud.abstraction.TaskFailed.with_traceback:1
#: tensorcircuit.cloud.abstraction.TaskUnfinished.with_traceback:1
#: tensorcircuit.cloud.utils.HttpStatusError.with_traceback:1
#: tensorcircuit.cons.NodesReturn.with_traceback:1
msgid ""
"Exception.with_traceback(tb) -- set self.__traceback__ to tb and return "
"self."
msgstr ""

#: of tensorcircuit.cloud.abstraction.Task:1
msgid "Task abstraction for quantum jobs on the cloud"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Task.details:1
msgid "Get the current task details"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Task.details:4
msgid "whether return until task is finished, defaults to False"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Task.details:6
#: tensorcircuit.cloud.abstraction.Task.results:10
msgid "alias for the argument ``blocked``"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Task.get_device:1
msgid "Query which device the task is run on"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Task.resubmit:1
msgid "resubmit the task"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Task.resubmit:3
msgid "the resubmitted task"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Task.results:1
msgid "get task results of the qjob"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Task.results:3
msgid "unsupported now, defaults to None, which is \"count_dict_bin\""
msgstr ""

#: of tensorcircuit.cloud.abstraction.Task.results:7
msgid ""
"whether blocked to wait until the result is returned, defaults to False, "
"which raise error when the task is unfinished"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Task.results:12
msgid "whether enable readout error mitigation, defaults to False"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Task.results:14
msgid "option dict for ``ReadoutMit.cals_from_system``, defaults to None"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Task.results:17
msgid ""
"if given, directly use the calibriation info on ``readout_mit``, defaults"
" to None"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Task.results:20
msgid "option dict for ``ReadoutMit.apply_correction``, defaults to None"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Task.results:22
msgid "count dict results"
msgstr ""

#: of tensorcircuit.cloud.abstraction.Task.state:1
msgid "Query the current task status"
msgstr ""

#: ../../source/api/cloud/apis.rst:2
msgid "tensorcircuit.cloud.apis"
msgstr ""

#: of tensorcircuit.cloud.apis:1
msgid "main entrypoints of cloud module"
msgstr ""

#: of tensorcircuit.cloud.apis.get_device:1
#: tensorcircuit.cloud.apis.set_device:1
msgid "set the default device"
msgstr ""

#: of tensorcircuit.cloud.apis.get_device:3
#: tensorcircuit.cloud.apis.set_device:3
msgid "provider of the device, defaults to None"
msgstr ""

#: of tensorcircuit.cloud.apis.get_device:5
#: tensorcircuit.cloud.apis.set_device:5
msgid "the device, defaults to None"
msgstr ""

#: of tensorcircuit.cloud.apis.get_device:7
#: tensorcircuit.cloud.apis.set_device:7
msgid ""
"whether set, defaults to True, if False, equivalent to ``get_device``, "
"defaults to True"
msgstr ""

#: of tensorcircuit.cloud.apis.get_provider:1
#: tensorcircuit.cloud.apis.set_provider:1
msgid "set default provider for the program"
msgstr ""

#: of tensorcircuit.cloud.apis.get_provider:5
#: tensorcircuit.cloud.apis.set_provider:5
msgid "whether set, defaults to True, if False, equivalent to ``get_provider``"
msgstr ""

#: of tensorcircuit.cloud.apis.get_task:1
msgid ""
"Get ``Task`` object from task string, the binding device can also be "
"provided"
msgstr ""

#: of tensorcircuit.cloud.apis.get_task_details:1
msgid "Get task details dict given task id"
msgstr ""

#: of tensorcircuit.cloud.apis.get_task_details:7
msgid ""
"whether make the returned dict more readable and more phythonic, defaults"
" to False"
msgstr ""

#: of tensorcircuit.cloud.apis.get_token:1
msgid ""
"Get API token setted for given provider or device, if no device token "
"saved, the corresponding provider tken is returned"
msgstr ""

#: of tensorcircuit.cloud.apis.list_devices:1
msgid "List all devices under a provider"
msgstr ""

#: of tensorcircuit.cloud.apis.list_properties:1
msgid "List properties of a given device"
msgstr ""

#: of tensorcircuit.cloud.apis.list_properties:9
msgid "Propeties dict"
msgstr ""

#: of tensorcircuit.cloud.apis.list_providers:1
msgid "list all cloud providers that tensorcircuit supports"
msgstr ""

#: of tensorcircuit.cloud.apis.list_tasks:1
msgid "List tasks based on given filters"
msgstr ""

#: of tensorcircuit.cloud.apis.list_tasks:9
msgid "list of task object that satisfy these filter criteria"
msgstr ""

#: of tensorcircuit.cloud.apis.resubmit_task:1
msgid "Rerun the given task"
msgstr ""

#: of tensorcircuit.cloud.apis.set_token:1
msgid "Set API token for given provider or specifically to given device"
msgstr ""

#: of tensorcircuit.cloud.apis.set_token:3
msgid "the API token, defaults to None"
msgstr ""

#: of tensorcircuit.cloud.apis.set_token:9
msgid "whether save on the disk, defaults to True"
msgstr ""

#: of tensorcircuit.cloud.apis.set_token:11
msgid "if True, clear all token saved, defaults to False"
msgstr ""

#: of tensorcircuit.cloud.apis.submit_task:1
msgid "submit task to the cloud platform, batch submission default enabled"
msgstr ""

#: of tensorcircuit.cloud.apis.submit_task:5
msgid ":py:meth:`tensorcircuit.cloud.tencent.submit_task`"
msgstr ""

#: of tensorcircuit.cloud.apis.submit_task:13
msgid ""
"all necessary keywords arguments for task submission, see detailed API in"
" each provider backend: 1. tencent - "
":py:meth:`tensorcircuit.cloud.tencent.submit_task`"
msgstr ""

#: of tensorcircuit.cloud.apis.submit_task:17
msgid "The task object"
msgstr ""

#: ../../source/api/cloud/config.rst:2
msgid "tensorcircuit.cloud.config"
msgstr ""

#: ../../source/api/cloud/local.rst:2
msgid "tensorcircuit.cloud.local"
msgstr ""

#: of tensorcircuit.cloud.local:1
msgid "Cloud provider from local machine"
msgstr ""

#: ../../source/api/cloud/quafu_provider.rst:2
msgid "tensorcircuit.cloud.quafu_provider"
msgstr ""

#: ../../source/api/cloud/tencent.rst:2
msgid "tensorcircuit.cloud.tencent"
msgstr ""

#: of tensorcircuit.cloud.tencent:1
msgid "Cloud provider from Tencent"
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:1
msgid ""
"Submit task via tencent provider, we suggest to enable one of the "
"compiling functionality: either in tc: frontend or in qos: backend. If "
"both are enabled, try on your own risk, some qubit mapping may fail "
"silently. If the user directly provide ``source`` or qiskit Circuit in "
"``circuit``, the qubit mapping should be taken care of by the users."
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:10
msgid "language choice for ``source``, defaults to \"OPENQASM\""
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:12
msgid "number of measurement shots, defaults to 1024"
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:14
msgid "submit task protocol version, defaults to \"1\""
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:16
msgid "priority for the task queue, defaults to 1"
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:18
msgid "tensorcircuit or qiskit circuit object, defaults to None"
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:20
msgid "directly given circuit representation, defaults to None"
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:22
msgid "remarks on the task, defaults to None"
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:24
msgid "whether compiling in tc via qiskit compiling system, defaults to False"
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:27
msgid "alias for the argument ``compiling``"
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:29
msgid "compiling options for qiskit ``transpile`` method, defaults to None"
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:32
msgid "alias for the argument ``compiled_options``"
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:34
msgid "whether to insert swap if necessary in qos, defaults to True"
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:36
msgid "whether to compile the gate in qos, defaults to True"
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:38
msgid "whether to run an initial qubit mapping in qos, defaults to False"
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:41
msgid ""
"when dry run, only compiled circuit is returned (no real circuit "
"execution), defaults to False"
msgstr ""

#: of tensorcircuit.cloud.tencent.submit_task:44
msgid "Task object or List of Task for batch submission"
msgstr ""

#: ../../source/api/cloud/utils.rst:2
msgid "tensorcircuit.cloud.utils"
msgstr ""

#: of tensorcircuit.cloud.utils:1
msgid "utility functions for cloud connection"
msgstr ""

#: of tensorcircuit.cloud.utils.HttpStatusError:1
msgid "Used when the return request has http code beyond 200"
msgstr ""

#: of tensorcircuit.cloud.utils.set_proxy:1
msgid ""
"str. format as \"http://user:passwd@host:port\" user passwd part can be "
"omitted if not set. None for turning off the proxy."
msgstr ""

#: ../../source/api/cloud/wrapper.rst:2
msgid "tensorcircuit.cloud.wrapper"
msgstr ""

#: of tensorcircuit.cloud.wrapper:1
msgid "higher level wrapper shortcut for submit_task"
msgstr ""

#: of tensorcircuit.cloud.wrapper.batch_expectation_ps:1
msgid ""
"Unified interface to compute the Pauli string expectation lists or sums "
"via simulation or on real qpu. Error mitigation, circuit compilation and "
"Pauli string grouping are all built-in."
msgstr ""

#: of tensorcircuit.cloud.wrapper.batch_expectation_ps:4
msgid ""
"One line access to unlock the whole power or real quantum hardware on "
"quantum cloud."
msgstr ""

#: of tensorcircuit.cloud.wrapper.batch_expectation_ps:18
msgid "The target circuit to compute expectation"
msgstr ""

#: of tensorcircuit.cloud.wrapper.batch_expectation_ps:20
msgid ""
"List of Pauli string list, eg. [[0, 1, 0], [2, 3, 3]] represents [X1, "
"Y0Z1Z2]."
msgstr ""

#: of tensorcircuit.cloud.wrapper.batch_expectation_ps:22
msgid ""
"The device str or object for quantum cloud module, defaults to None, None"
" is for analytical exact simulation"
msgstr ""

#: of tensorcircuit.cloud.wrapper.batch_expectation_ps:25
msgid ""
"List of float to indicate the final return is the weighted sum of Pauli "
"string expectations, e.g. [2., -0.3] represents the final results is 2* "
"``pss`` [0]-0.3* ``pss`` [1] defaults to None, None indicate the list of "
"expectations for ``pss`` are all returned"
msgstr ""

#: of tensorcircuit.cloud.wrapper.batch_expectation_ps:29
msgid "measurement shots for each expectation estimation, defaults to 8192"
msgstr ""

#: of tensorcircuit.cloud.wrapper.batch_expectation_ps:31
msgid "whether enable readout error mitigation for the result, defaults to True"
msgstr ""

#: of tensorcircuit.cloud.wrapper.batch_expectation_ps:33
msgid ""
"List of Pauli string expectation or a weighted sum float for Pauli "
"strings, depending on ``ws``"
msgstr ""

#: of tensorcircuit.cloud.wrapper.sample_expectation_ps:1
msgid ""
"Deprecated, please use "
":py:meth:`tensorcircuit.cloud.wrapper.batch_expectation_ps`."
msgstr ""

#: ../../source/api/compiler.rst:2
msgid "tensorcircuit.compiler"
msgstr ""

#: ../../source/api/compiler/composed_compiler.rst:2
msgid "tensorcircuit.compiler.composed_compiler"
msgstr ""

#: of tensorcircuit.compiler.composed_compiler:1
msgid "object oriented compiler pipeline"
msgstr ""

#: of tensorcircuit.compiler.composed_compiler.DefaultCompiler.__init__:1
msgid ""
"A fallback choice to compile circuit running on tencent quantum cloud "
"with rz as native gate"
msgstr ""

#: of tensorcircuit.compiler.composed_compiler.DefaultCompiler.__init__:3
#, python-brace-format
msgid ""
"qiskit compiled options to be added options documented in "
"`qiskit.transpile` method, to use tencent quantum cloud, "
"`{\"coupling_map\": d.topology()}` is in general enough, where d is a "
"device object, defaults to None, i.e. no qubit mapping is applied"
msgstr ""

#: ../../source/api/compiler/qiskit_compiler.rst:2
msgid "tensorcircuit.compiler.qiskit_compiler"
msgstr ""

#: of tensorcircuit.compiler.qiskit_compiler:1
msgid "compiler interface via qiskit"
msgstr ""

#: of tensorcircuit.compiler.qiskit_compiler.qiskit_compile:1
msgid ""
"compile the circuit using ``qiskit.transpile`` method with some tricks "
"and hacks"
msgstr ""

#: of tensorcircuit.compiler.qiskit_compiler.qiskit_compile:3
msgid "circuit in ``tc.Circuit`` or ``qiskit.QuantumCircuit`` form"
msgstr ""

#: of tensorcircuit.compiler.qiskit_compiler.qiskit_compile:5
msgid "info for qubit mappings, defaults to None"
msgstr ""

#: of tensorcircuit.compiler.qiskit_compiler.qiskit_compile:7
msgid "output circuit format, defaults to \"tc\""
msgstr ""

#: of tensorcircuit.compiler.qiskit_compiler.qiskit_compile:9
msgid "``qiskit.transpile`` options in a dict, defaults to None"
msgstr ""

#: of tensorcircuit.compiler.qiskit_compiler.qiskit_compile:11
msgid "Tuple containing the output circuit and the qubit mapping info dict"
msgstr ""

#: ../../source/api/compiler/simple_compiler.rst:2
msgid "tensorcircuit.compiler.simple_compiler"
msgstr ""

#: of tensorcircuit.compiler.simple_compiler:1
msgid "Very simple transformations that qiskit may even fail or hard to control"
msgstr ""

#: ../../source/api/cons.rst:2
msgid "tensorcircuit.cons"
msgstr ""

#: of tensorcircuit.cons:1
msgid "Constants and setups"
msgstr ""

#: of tensorcircuit.cons.NodesReturn:1
msgid "Intentionally stop execution to return a value."
msgstr ""

#: of tensorcircuit.cons.OMEOptimizer:1
msgid "Adapt an ``omeco`` optimizer for ``tc.set_contractor(\"custom\", ...)``."
msgstr ""

#: of tensorcircuit.cons.OMEOptimizer:3
msgid ""
"``omeco`` stays an optional dependency and is imported lazily only when "
"this adapter is actually used."
msgstr ""

#: of tensorcircuit.cons.contraction_info_decorator:1
msgid "Decorator to add contraction information logging to an optimizer."
msgstr ""

#: of tensorcircuit.cons.contraction_info_decorator:3
msgid ""
"This decorator wraps an optimization algorithm and prints detailed "
"information about the contraction cost (FLOPs, size, write) and path "
"finding time."
msgstr ""

#: of tensorcircuit.cons.contraction_info_decorator:6
msgid "The optimization algorithm to decorate."
msgstr ""

#: of tensorcircuit.cons.contraction_info_decorator:8
msgid "The decorated optimization algorithm."
msgstr ""

#: of tensorcircuit.cons.get_contractor:1 tensorcircuit.cons.set_contractor:1
msgid ""
"To set runtime contractor of the tensornetwork for a better contraction "
"path. For more information on the usage of contractor, please refer to "
"independent tutorial."
msgstr ""

#: of tensorcircuit.cons.get_contractor:4 tensorcircuit.cons.set_contractor:4
msgid ""
"\"auto\", \"greedy\", \"branch\", \"plain\", \"tng\", \"custom\", "
"\"custom_stateful\". defaults to None (\"auto\")"
msgstr ""

#: of tensorcircuit.cons.get_contractor:6 tensorcircuit.cons.set_contractor:6
msgid "Valid for \"custom\" or \"custom_stateful\" as method, defaults to None"
msgstr ""

#: of tensorcircuit.cons.get_contractor:8 tensorcircuit.cons.set_contractor:8
msgid ""
"It is not very useful, as ``memory_limit`` leads to ``branch`` "
"contraction instead of ``greedy`` which is rather slow, defaults to None"
msgstr ""

#: of tensorcircuit.cons.get_contractor:11 tensorcircuit.cons.set_contractor:11
msgid "Tensornetwork version is too low to support some of the contractors."
msgstr ""

#: of tensorcircuit.cons.get_contractor:12 tensorcircuit.cons.set_contractor:12
msgid "Unknown method options."
msgstr ""

#: of tensorcircuit.cons.get_contractor:13 tensorcircuit.cons.set_contractor:13
msgid "The new tensornetwork with its contractor set."
msgstr ""

#: of tensorcircuit.cons.get_dtype:1 tensorcircuit.cons.set_dtype:1
msgid "Set the global runtime numerical dtype of tensors."
msgstr ""

#: of tensorcircuit.cons.get_dtype:3 tensorcircuit.cons.set_dtype:3
msgid ""
"\"complex64\"/\"float32\" or \"complex128\"/\"float64\", defaults to "
"None, which is equivalent to \"complex64\"."
msgstr ""

#: of tensorcircuit.cons.get_dtype:6 tensorcircuit.cons.set_dtype:6
msgid "complex dtype str and the corresponding real dtype str"
msgstr ""

#: of tensorcircuit.cons.get_symbol:1
msgid ""
"Get the symbol corresponding to int ``i`` - runs through the usual 52 "
"letters before resorting to unicode characters, starting at ``chr(192)`` "
"and skipping surrogates. From cotengra codebase"
msgstr ""

#: of tensorcircuit.cons.get_tn_info:1
msgid "import cotengra as ctg import opt_einsum as oem"
msgstr ""

#: of tensorcircuit.cons.get_tn_info:4
msgid "sys.setrecursionlimit(10000) # for successfullt ctg parallel"
msgstr ""

#: of tensorcircuit.cons.get_tn_info:6
msgid "opt = ctg.ReusableHyperOptimizer("
msgstr ""

#: of tensorcircuit.cons.get_tn_info:7
msgid ""
"methods=[\"greedy\", \"kahypar\"], parallel=True, minimize=\"write\", "
"max_time=30, max_repeats=4096, progbar=True,"
msgstr ""

#: of tensorcircuit.cons.get_tn_info:13
msgid ""
") tc.set_contractor(\"custom\", optimizer=opt, preprocessing=True) "
"tc.set_contractor(\"custom_stateful\", optimizer=oem.RandomGreedy, "
"max_time=60, max_repeats=128, minimize=\"size\") tc.set_contractor"
"(\"plain-experimental\", local_steps=3)"
msgstr ""

#: of tensorcircuit.cons.get_tn_info:18
msgid ""
"# hyper efficient contractor: though long computation time required, "
"suitable for extra large circuit simulation opt = "
"ctg.ReusableHyperOptimizer("
msgstr ""

#: of tensorcircuit.cons.get_tn_info:20
msgid ""
"minimize='combo', max_repeats=1024, max_time='equil:128', "
"optlib='nevergrad', progbar=True,"
msgstr ""

#: of tensorcircuit.cons.get_tn_info:27
msgid "def opt_reconf(inputs, output, size, **kws):"
msgstr ""

#: of tensorcircuit.cons.get_tn_info:28
msgid ""
"tree = opt.search(inputs, output, size) tree_r = "
"tree.subtree_reconfigure_forest(progbar=True, num_trees=10,"
msgstr ""

#: of tensorcircuit.cons.get_tn_info:30
msgid "num_restarts=20, subtree_weight_what=(\"size\", ))"
msgstr ""

#: of tensorcircuit.cons.get_tn_info:31
msgid "return tree_r.get_path()"
msgstr ""

#: of tensorcircuit.cons.get_tn_info:33
msgid "tc.set_contractor(\"custom\", optimizer=opt_reconf)"
msgstr ""

#: of tensorcircuit.cons.plain_contractor:1
msgid "The naive state-vector simulator contraction path."
msgstr ""

#: of tensorcircuit.cons.plain_contractor:3
msgid "The list of ``tn.Node``."
msgstr ""

#: of tensorcircuit.cons.plain_contractor:5
msgid "The list of dangling node edges, defaults to be None."
msgstr ""

#: of tensorcircuit.cons.plain_contractor:7
msgid "The ``tn.Node`` after contraction"
msgstr ""

#: of tensorcircuit.cons.runtime_backend:1
msgid "Context manager to set with-level runtime backend"
msgstr ""

#: of tensorcircuit.cons.runtime_backend:3
#: tensorcircuit.cons.set_function_backend:3
msgid "\"numpy\", \"tensorflow\", \"jax\", \"pytorch\", defaults to None"
msgstr ""

#: of tensorcircuit.cons.runtime_backend tensorcircuit.cons.runtime_contractor
#: tensorcircuit.cons.runtime_dtype
msgid "yield"
msgstr ""

#: of tensorcircuit.cons.runtime_backend:5
msgid "the backend object"
msgstr ""

#: of tensorcircuit.cons.runtime_contractor:1
msgid "Context manager to change with-levek contractor"
msgstr ""

#: of tensorcircuit.cons.runtime_dtype:1
msgid "Context manager to set with-level runtime dtype"
msgstr ""

#: of tensorcircuit.cons.runtime_dtype:3
msgid "\"complex64\" or \"complex128\", defaults to None (\"complex64\")"
msgstr ""

#: of tensorcircuit.cons.runtime_dtype:5
msgid "complex dtype str and real dtype str"
msgstr ""

#: of tensorcircuit.cons.set_tensornetwork_backend:1
msgid "To set the runtime backend of tensorcircuit."
msgstr ""

#: of tensorcircuit.cons.set_tensornetwork_backend:3
msgid ""
"Note: ``tc.set_backend`` and ``tc.cons.set_tensornetwork_backend`` are "
"the same."
msgstr ""

#: of tensorcircuit.cons.set_tensornetwork_backend:27
msgid ""
"\"numpy\", \"tensorflow\", \"jax\", \"pytorch\". defaults to None, which "
"gives the same behavior as "
"``tensornetwork.backend_contextmanager.get_default_backend()``."
msgstr ""

#: of tensorcircuit.cons.set_tensornetwork_backend:30
msgid "Whether the object should be set as global."
msgstr ""

#: of tensorcircuit.cons.set_function_backend:1
msgid "Function decorator to set function-level runtime backend"
msgstr ""

#: of tensorcircuit.cons.set_function_backend:5
msgid "Decorated function"
msgstr ""

#: of tensorcircuit.cons.set_function_contractor:1
msgid "Function decorate to change function-level contractor"
msgstr ""

#: of tensorcircuit.cons.set_function_dtype:1
msgid "Function decorator to set function-level numerical dtype"
msgstr ""

#: of tensorcircuit.cons.set_function_dtype:3
msgid "\"complex64\" or \"complex128\", defaults to None"
msgstr ""

#: of tensorcircuit.cons.set_function_dtype:5
msgid "The decorated function"
msgstr ""

#: of tensorcircuit.cons.split_rules:1
msgid "Obtain the direcionary of truncation rules"
msgstr ""

#: of tensorcircuit.cons.split_rules:3
#: tensorcircuit.mps_base.FiniteMPS.apply_two_site_gate:11
msgid "The maximum number of singular values to keep."
msgstr ""

#: of tensorcircuit.cons.split_rules:5
#: tensorcircuit.mps_base.FiniteMPS.apply_two_site_gate:13
msgid "The maximum allowed truncation error."
msgstr ""

#: ../../source/api/densitymatrix.rst:2
msgid "tensorcircuit.densitymatrix"
msgstr ""

#: of tensorcircuit.densitymatrix:1
msgid "Quantum circuit class but with density matrix simulator"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.__init__:1
msgid ""
"The density matrix simulator based on tensornetwork engine. Do not use "
"this class with d!=2 directly"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.__init__:4
#: tensorcircuit.stabilizercircuit.StabilizerCircuit.__init__:3
msgid "Number of qubits"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.__init__:6
msgid "if True, nothing initialized, only for internal use, defaults to False"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.__init__:8
msgid "the state input for the circuit, defaults to None"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.__init__:10
msgid "QuVector for a MPS like initial pure state."
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.__init__:12
msgid ""
"Sequence of tensors for a MPS like initial pure state. The order of legs "
"for each tensor is assumed to be (bond-left, physical, bond-right)."
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.__init__:15
msgid "the density matrix input for the circuit, defaults to None"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.__init__:17
msgid "QuOperator for a MPO like initial density matrix."
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.check_kraus:1
msgid ""
"Check if Kraus operators satisfy the completeness relation: "
":math:`\\sum_i K_i^\\dagger K_i = I`"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.check_kraus:4
msgid "Sequence of Kraus operators"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.check_kraus:6
msgid "True if completeness relation is satisfied within tolerance"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.check_kraus:8
msgid "If completeness relation is not satisfied"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.densitymatrix:1
msgid "Return the output density matrix of the circuit."
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.densitymatrix:3
msgid ""
"check whether the final return is a legal density matrix, defaults to "
"False"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.densitymatrix:5
msgid "whether to reuse previous results, defaults to True"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.densitymatrix:7
msgid "The output densitymatrix in 2D shape tensor form"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.expectation:1
msgid "Compute the expectation of corresponding operators."
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.expectation:6
msgid "whether contract the density matrix in advance, defaults to True"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.get_dm_as_quoperator:1
msgid ""
"Get the representation of the output state in the form of ``QuOperator`` "
"while maintaining the circuit uncomputed"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.get_dm_as_quoperator:4
msgid "``QuOperator`` representation of the output state from the circuit"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.to_circuit:1
msgid ""
"convert into state simulator (current implementation ignores all noise "
"channels)"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.to_circuit:4
msgid "kws to initialize circuit object, defaults to None"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.to_circuit:7
msgid "Circuit with no noise"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.wavefunction:1
msgid ""
"get the wavefunction of outputs, raise error if the final state is not "
"purified [Experimental: the phase factor is not fixed for different "
"backend]"
msgstr ""

#: of tensorcircuit.densitymatrix.DMCircuit.wavefunction:5
msgid "wavefunction vector"
msgstr ""

#: ../../source/api/experimental.rst:2
msgid "tensorcircuit.experimental"
msgstr ""

#: of tensorcircuit.experimental:1
msgid "Experimental features"
msgstr ""

#: of tensorcircuit.experimental.DistributedContractor:1
msgid ""
"A distributed tensor network contractor that parallelizes computations "
"across multiple devices."
msgstr ""

#: of tensorcircuit.experimental.DistributedContractor:3
msgid ""
"This class uses cotengra to find optimal contraction paths and "
"distributes the computational load across multiple devices (e.g., GPUs) "
"for efficient tensor network calculations. Particularly useful for large-"
"scale quantum circuit simulations and variational quantum algorithms."
msgstr ""

#: of tensorcircuit.experimental.DistributedContractor:16
msgid "Function that takes parameters and returns a list of tensor network nodes"
msgstr ""

#: of tensorcircuit.experimental.DistributedContractor:18
msgid "Initial parameters used to determine the tensor network structure"
msgstr ""

#: of tensorcircuit.experimental.DistributedContractor:20
msgid "Configuration options passed to the cotengra optimizer. Defaults to None"
msgstr ""

#: of tensorcircuit.experimental.DistributedContractor:22
msgid "List of devices to use. If None, uses all available devices"
msgstr ""

#: of tensorcircuit.experimental.DistributedContractor:24
msgid ""
"Mesh object to use for distributed computation. If None, uses all "
"available devices"
msgstr ""

#: of tensorcircuit.experimental.DistributedContractor.value:1
msgid "Calculates the value, compiling the pmap function for the first call."
msgstr ""

#: of tensorcircuit.experimental.DistributedContractor.value:3
#: tensorcircuit.experimental.DistributedContractor.value_and_grad:3
msgid "Parameters for the `nodes_fn` input"
msgstr ""

#: of tensorcircuit.experimental.DistributedContractor.value:5
msgid ""
"Optional post-processing function for the output, defaults to None "
"(corresponding to identity) op is a cache key, so dont directly pass "
"lambda function for op"
msgstr ""

#: of tensorcircuit.experimental.DistributedContractor.value:8
msgid ""
"dtype str for the output of `nodes_fn`, defaults to None (corresponding "
"to `dtypestr`)"
msgstr ""

#: of tensorcircuit.experimental.DistributedContractor.value_and_grad:1
msgid ""
"Calculates the value and gradient, compiling the pmap function if needed "
"for the first call."
msgstr ""

#: of tensorcircuit.experimental.DistributedContractor.value_and_grad:5
msgid ""
"Optional post-processing function for the output, defaults to None "
"(corresponding to `backend.real`) op is a cache key, so dont directly "
"pass lambda function for op"
msgstr ""

#: of tensorcircuit.experimental.DistributedContractor.value_and_grad:8
msgid ""
"dtype str for the output of `nodes_fn`, defaults to None (corresponding "
"to `rdtypestr`)"
msgstr ""

#: of tensorcircuit.experimental.adaptive_vmap:1
msgid "Vectorized map with adaptive chunking for memory efficiency."
msgstr ""

#: of tensorcircuit.experimental.adaptive_vmap:3
msgid "Function to be vectorized"
msgstr ""

#: of tensorcircuit.experimental.adaptive_vmap:4
msgid "Arguments to be vectorized over"
msgstr ""

#: of tensorcircuit.experimental.adaptive_vmap:5
msgid "Arguments that remain static during vectorization"
msgstr ""

#: of tensorcircuit.experimental.adaptive_vmap:6
msgid "Size of chunks for batch processing, None means no chunking (naive vmap)"
msgstr ""

#: of tensorcircuit.experimental.adaptive_vmap:8
msgid "Vectorized function"
msgstr ""

#: of tensorcircuit.experimental.broadcast_py_object:1
msgid ""
"Broadcast a picklable Python object from process 0 to all other "
"processes, with fallback mechanism from gRPC to file system based "
"approach."
msgstr ""

#: of tensorcircuit.experimental.broadcast_py_object:4
msgid ""
"This function first attempts to use gRPC-based broadcast. If that fails "
"due to pickling issues, it falls back to a file system based approach "
"that is more robust."
msgstr ""

#: of tensorcircuit.experimental.broadcast_py_object:7
#: tensorcircuit.experimental.broadcast_py_object_jax:8
msgid ""
"The Python object to broadcast. It must be picklable. This object should "
"exist on process 0 and can be None on others."
msgstr ""

#: of tensorcircuit.experimental.broadcast_py_object:10
msgid ""
"Directory path for shared file system broadcast fallback. If None, uses "
"current directory. Only used in fallback mode."
msgstr ""

#: of tensorcircuit.experimental.broadcast_py_object:13
#: tensorcircuit.experimental.broadcast_py_object_fs:16
#: tensorcircuit.experimental.broadcast_py_object_jax:11
msgid "The broadcasted object, now present on all processes."
msgstr ""

#: of tensorcircuit.experimental.broadcast_py_object_fs:1
msgid ""
"Broadcast a picklable Python object from process 0 to all other processes"
" using a shared file system approach."
msgstr ""

#: of tensorcircuit.experimental.broadcast_py_object_fs:4
msgid ""
"This is a fallback method when gRPC-based broadcast fails. It uses UUID-"
"based file communication to share objects between processes through a "
"shared file system."
msgstr ""

#: of tensorcircuit.experimental.broadcast_py_object_fs:7
msgid ""
"The Python object to broadcast. Must be picklable. Should exist on "
"process 0, can be None on others."
msgstr ""

#: of tensorcircuit.experimental.broadcast_py_object_fs:10
msgid ""
"Directory path for shared file system communication. If None, uses "
"current directory."
msgstr ""

#: of tensorcircuit.experimental.broadcast_py_object_fs:13
msgid ""
"Maximum time to wait for file operations before timing out. Defaults to "
"300 seconds."
msgstr ""

#: of tensorcircuit.experimental.broadcast_py_object_jax:1
msgid ""
"Broadcast a picklable Python object from process 0 to all other processes"
" within jax ditribution system."
msgstr ""

#: of tensorcircuit.experimental.broadcast_py_object_jax:4
msgid ""
"This function uses a two-step broadcast: first the size, then the data. "
"This is necessary because `broadcast_one_to_all` requires the same shaped"
" array on all hosts."
msgstr ""

#: of tensorcircuit.experimental.dynamics_matrix:1
#: tensorcircuit.experimental.qng:1
msgid "Compute quantum natural gradient for quantum circuit optimization."
msgstr ""

#: of tensorcircuit.experimental.dynamics_matrix:3
#: tensorcircuit.experimental.qng:3
msgid "Function that takes parameters and returns quantum state"
msgstr ""

#: of tensorcircuit.experimental.dynamics_matrix:4
#: tensorcircuit.experimental.qng:4
msgid ""
"Type of kernel to use (\"qng\" or \"dynamics\"), the former has the "
"second term"
msgstr ""

#: of tensorcircuit.experimental.dynamics_matrix:5
#: tensorcircuit.experimental.qng:5
msgid "Post-processing method (\"qng\" or None)"
msgstr ""

#: of tensorcircuit.experimental.dynamics_matrix:6
#: tensorcircuit.experimental.qng:6
msgid "Mode of differentiation (\"fwd\" or \"rev\")"
msgstr ""

#: of tensorcircuit.experimental.dynamics_matrix:7
#: tensorcircuit.experimental.qng:7
msgid "Function computing QNG matrix"
msgstr ""

#: of tensorcircuit.experimental.jax_jitted_function_load:1
msgid "load a jitted function from file"
msgstr ""

#: of tensorcircuit.experimental.jax_jitted_function_load:5
msgid "the loaded function"
msgstr ""

#: of tensorcircuit.experimental.jax_jitted_function_save:1
msgid "save a jitted jax function as a file"
msgstr ""

#: of tensorcircuit.experimental.jax_jitted_function_save:5
msgid "the jitted function"
msgstr ""

#: of tensorcircuit.experimental.jax_jitted_function_save:7
msgid "example function arguments for ``f``"
msgstr ""

#: of tensorcircuit.experimental.parameter_shift_grad:1
msgid ""
"similar to `grad` function but using parameter shift internally instead "
"of AD, vmap is utilized for evaluation, so the speed is still ok"
msgstr ""

#: of tensorcircuit.experimental.parameter_shift_grad:4
#: tensorcircuit.experimental.parameter_shift_grad_v2:6
msgid "quantum function with weights in and expectation out"
msgstr ""

#: of tensorcircuit.experimental.parameter_shift_grad:6
#: tensorcircuit.experimental.parameter_shift_grad_v2:8
msgid "label which args should be differentiated, defaults to 0"
msgstr ""

#: of tensorcircuit.experimental.parameter_shift_grad:9
#: tensorcircuit.experimental.parameter_shift_grad_v2:11
msgid "whether jit the original function `f` at the beginning, defaults to False"
msgstr ""

#: of tensorcircuit.experimental.parameter_shift_grad:12
#: tensorcircuit.experimental.parameter_shift_grad_v2:28
msgid ""
"two floats for the delta shift on the numerator and dominator, defaults "
"to (pi/2, 2) for parameter shift"
msgstr ""

#: of tensorcircuit.experimental.parameter_shift_grad:15
#: tensorcircuit.experimental.parameter_shift_grad_v2:31
msgid "the grad function"
msgstr ""

#: of tensorcircuit.experimental.parameter_shift_grad_v2:1
msgid ""
"similar to `grad` function but using parameter shift internally instead "
"of AD, vmap is utilized for evaluation, v2 also supports random generator"
" for finite measurememt shot, only jax backend is supported, since no "
"vmap randomness is available in tensorflow"
msgstr ""

#: of tensorcircuit.experimental.parameter_shift_grad_v2:14
msgid ""
"label which args should be treated as random sources, including JAX PRNG "
"keys or uniform random status tensors, defaults to None. If a JAX PRNG "
"key is provided, it is split into independent subkeys for each shifted "
"circuit instance evaluation. If a status tensor is provided, it should be"
" pre-batched to match the shifted circuit instances. Specifically, if the"
" status tensor has shape [2, size, ...], the first slice [0, size, ...] "
"is used for the positive shifts and the second slice [1, size, ...] is "
"used for the negative shifts. If the status tensor has shape [size, ...],"
" it is used for both positive and negative shifts (Common Random "
"Numbers). Here `size` refers to the total number of elements in the "
"parameter tensor being differentiated (i.e., "
"`backend.sizen(args[argnum])`)."
msgstr ""

#: of tensorcircuit.experimental.parameter_shift_grad_v2:35
msgid ""
"While the wrapper logic is backend-agnostic, stochastic functions (like "
"those using `sample`) may only be differentiable via this wrapper on the "
"JAX backend due to limited `vmap` support for such operations in other "
"backends."
msgstr ""

#: ../../source/api/fgs.rst:2
msgid "tensorcircuit.fgs"
msgstr ""

#: of tensorcircuit.fgs:1
msgid "Fermion Gaussian state simulator"
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator:1
msgid ""
"Fermion Gaussian State (FGS) simulator for efficient simulation of non-"
"interacting fermionic systems."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator:3
msgid ""
"This simulator implements methods for: 1. State initialization and "
"evolution 2. Correlation matrix calculations 3. Entanglement measures 4. "
"Out-of-time-ordered correlators (OTOC) 5. Post-selection and measurements"
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator:10
msgid ""
"Main references: - Gaussian formalism: "
"https://arxiv.org/pdf/2306.16595.pdf - Quantum circuits: "
"https://arxiv.org/abs/2209.06945 - Theory background: "
"https://scipost.org/SciPostPhysLectNotes.54/pdf"
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator:15
msgid ""
"Conventions: - Hamiltonian form: (c^dagger, c)H(c, c^\\dagger) - "
"Correlation form: <(c, c^\\dagger)(c^\\dagger, c)> - Bogoliubov "
"transformation: c' = \\alpha^\\dagger (c, c^\\dagger)"
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.__init__:1
msgid "Initializes the Fermion Gaussian State (FGS) simulator."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.__init__:3
msgid ""
"The state can be initialized in one of four ways: 1. By specifying the "
"system size `L` and a list of `filled` sites, creating a product state. "
"2. By providing a quadratic Hamiltonian `hc`, the ground state of which "
"is used as the initial state. 3. By directly providing the `alpha` "
"matrix, which defines the Bogoliubov transformation. 4. For debugging, by"
" providing a pre-computed correlation matrix `cmatrix`."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.__init__:9
msgid "The number of fermionic sites (system size)."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.__init__:11
msgid ""
"A list of site indices that are occupied by fermions in the initial "
"state. Defaults to None (no sites filled)."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.__init__:14
msgid ""
"The matrix defining the Bogoliubov transformation from the vacuum state, "
"of shape `(2L, L)`. If provided, it directly specifies the initial state."
" Defaults to None."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.__init__:17
msgid ""
"A quadratic Hamiltonian. The ground state of this Hamiltonian will be "
"used as the initial state. Defaults to None."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.__init__:20
msgid ""
"A pre-computed correlation matrix, primarily for debugging purposes. "
"Defaults to None."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.charge_moment:1
msgid "Computes the charge moment of order n."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.charge_moment:3
#: tensorcircuit.fgs.FGSSimulator.renyi_entanglement_asymmetry:3
msgid "Ref: https://arxiv.org/abs/2302.03330"
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.charge_moment:5
msgid "The alpha parameter for the charge moment calculation."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.charge_moment:7
msgid "The order of the charge moment (Renyi-n)."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.charge_moment:9
#: tensorcircuit.fgs.FGSSimulator.get_reduced_cmatrix:7
#: tensorcircuit.fgs.FGSSimulator.renyi_entanglement_asymmetry:7
msgid "A list of site indices to be traced out."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.charge_moment:11
msgid "The charge moment."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.chemical_potential:1
msgid "Constructs the chemical potential Hamiltonian for a single site."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.chemical_potential:3
msgid ""
"The chemical potential Hamiltonian is given by :math:`\\chi c_i^\\dagger "
"c_i`."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.chemical_potential:5
msgid "The chemical potential strength."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.chemical_potential:7
#: tensorcircuit.fgs.FGSSimulator.evol_cp:8
#: tensorcircuit.fgs.FGSSimulator.evol_icp:8
msgid "The index of the site."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.chemical_potential:9
#: tensorcircuit.fgs.FGSSimulator.fermion_diagonalization:8
#: tensorcircuit.fgs.FGSSimulator.fermion_diagonalization_2:8
#: tensorcircuit.fgs.FGSSimulator.hopping:11
#: tensorcircuit.fgs.FGSSimulator.init_alpha:5
#: tensorcircuit.fgs.FGSSimulator.sc_pairing:11
#: tensorcircuit.fgs.FGSSimulator.wmatrix:5
msgid "The number of fermionic sites."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.chemical_potential:11
msgid "The chemical potential Hamiltonian."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.cond_measure:1
msgid ""
"Performs a conditional measurement on a specific site. The fermion "
"Gaussian state is collapsed in the consistent way accordingly."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.cond_measure:4
msgid "The index of the site to measure."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.cond_measure:6
msgid "A random number between 0 and 1 to determine the measurement outcome."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.cond_measure:8
msgid ""
"If True, also return the probabilities of the measurement outcomes. "
"Defaults to False."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.cond_measure:10
msgid ""
"The measurement outcome (0 or 1). If `with_prob` is True, a tuple "
"containing the outcome and the probabilities is returned."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.entropy:1
msgid "Computes the von Neumann entropy of a subsystem."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.entropy:3
msgid ""
"A list of site indices to be traced out, defining the subsystem. If None,"
" the entropy of the entire system is computed. Defaults to None."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.entropy:6
msgid "The von Neumann entropy."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_cp:1
msgid "Evolves the state with a chemical potential Hamiltonian."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_cp:3
msgid ""
"The evolution is governed by the Hamiltonian :math:`\\chi c_i^\\dagger "
"c_i`."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_cp:5
msgid ""
"This optimized implementation uses analytical phase factors reducing "
"complexity from O(L³) to O(L)."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_cp:10
#: tensorcircuit.fgs.FGSSimulator.evol_icp:10
msgid "The chemical potential strength. Defaults to 0."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_ghamiltonian:1
msgid "Evolves the state with a general non-Hermitian Hamiltonian."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_ghamiltonian:3
msgid "The evolution is given by :math:`e^{-1/2 i \\hat{h}}`."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_ghamiltonian:5
msgid "The non-Hermitian Hamiltonian for the evolution."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_hamiltonian:1
msgid "Evolves the state with a given Hamiltonian."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_hamiltonian:3
msgid "The evolution is given by :math:`e^{-i/2 \\hat{h}}`."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_hamiltonian:5
#: tensorcircuit.fgs.FGSSimulator.evol_ihamiltonian:5
msgid "The Hamiltonian for the evolution."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_hp:1
msgid "Evolves the state with a hopping Hamiltonian."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_hp:3
msgid ""
"The evolution is governed by the Hamiltonian :math:`\\chi c_i^\\dagger "
"c_j + h.c.`."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_hp:5
msgid ""
"This optimized implementation uses analytical 2x2 local unitaries "
"reducing complexity from O(L³) to O(L)."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_hp:8
#: tensorcircuit.fgs.FGSSimulator.evol_sp:8
#: tensorcircuit.fgs.FGSSimulator.hopping:7
#: tensorcircuit.fgs.FGSSimulator.sc_pairing:7
msgid "The index of the first site."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_hp:10
#: tensorcircuit.fgs.FGSSimulator.evol_sp:10
#: tensorcircuit.fgs.FGSSimulator.hopping:9
#: tensorcircuit.fgs.FGSSimulator.sc_pairing:9
msgid "The index of the second site."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_hp:12
msgid "The hopping strength. Defaults to 0."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_icp:1
msgid ""
"Evolves the state with a chemical potential Hamiltonian using imaginary "
"time evolution."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_icp:3
#, python-brace-format
msgid ""
"The evolution is governed by :math:`e^{-H/2}` where :math:`H = \\chi "
"c_i^\\dagger c_i`."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_icp:5
msgid ""
"This optimized implementation uses analytical scaling factors reducing "
"complexity from O(L³) to O(L)."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_ihamiltonian:1
msgid "Evolves the state with a given Hamiltonian using imaginary time evolution."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_ihamiltonian:3
msgid "The evolution is given by :math:`e^{-1/2 \\hat{h}}`."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_sp:1
msgid "Evolves the state with a superconducting pairing Hamiltonian."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_sp:3
msgid ""
"The evolution is governed by the Hamiltonian :math:`\\chi c_i^\\dagger "
"c_j^\\dagger + h.c.`."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_sp:5
msgid ""
"This optimized implementation uses analytical 4x4 local unitaries "
"reducing complexity from O(L³) to O(L)."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.evol_sp:12
msgid "The pairing strength. Defaults to 0."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.expectation_2body:1
msgid "Calculates the expectation value of a two-fermion term."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.expectation_2body:3
#, python-brace-format
msgid ""
"The convention for the operators is (c, c^\\dagger). For i >= L, the "
"operator is c_{i-L}^\\dagger."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.expectation_2body:5
#: tensorcircuit.fgs.FGSSimulator.expectation_4body:5
msgid "The index of the first fermion operator."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.expectation_2body:7
#: tensorcircuit.fgs.FGSSimulator.expectation_4body:7
msgid "The index of the second fermion operator."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.expectation_2body:9
msgid "Whether to use the current state for the first operator. Defaults to True."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.expectation_2body:11
msgid ""
"Whether to use the current state for the second operator. Defaults to "
"True."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.expectation_2body:13
msgid "The expectation value of the two-fermion term."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.expectation_4body:1
msgid ""
"Calculates the expectation value of a four-fermion term using Wick's "
"theorem."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.expectation_4body:3
#, python-brace-format
msgid ""
"The convention for the operators is (c, c^\\dagger). For an index m >= L,"
" the operator is c_{m-L}^\\dagger."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.expectation_4body:9
msgid "The index of the third fermion operator."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.expectation_4body:11
msgid "The index of the fourth fermion operator."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.expectation_4body:13
msgid "The expectation value of the four-fermion term."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.fermion_diagonalization:1
msgid "Diagonalizes a quadratic fermionic Hamiltonian."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.fermion_diagonalization:3
msgid ""
"This method computes the eigenvalues, eigenvectors, and the alpha matrix "
"for a given quadratic Hamiltonian `hc`."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.fermion_diagonalization:6
#: tensorcircuit.fgs.FGSSimulator.fermion_diagonalization_2:6
msgid "The quadratic Hamiltonian to be diagonalized."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.fermion_diagonalization:10
#: tensorcircuit.fgs.FGSSimulator.fermion_diagonalization_2:10
msgid "A tuple containing the eigenvalues, eigenvectors, and the alpha matrix."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.fermion_diagonalization_2:1
msgid "Alternative method for diagonalizing a quadratic fermionic Hamiltonian."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.fermion_diagonalization_2:3
msgid ""
"This method uses a different approach based on the Schur decomposition to"
" diagonalize the Hamiltonian."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_alpha:1
msgid "Returns the current alpha matrix of the FGS."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_alpha:3
msgid "The alpha matrix."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_bogoliubov_uv:1
msgid "Returns the u and v matrices of the Bogoliubov transformation."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_bogoliubov_uv:3
#, python-brace-format
msgid ""
"The Bogoliubov transformation is defined as: :math:`b_k = u_{k,i} a_i + "
"v_{k,i} a_i^\\dagger`"
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_bogoliubov_uv:6
msgid ""
"where :math:`b_k` are the new fermionic operators and :math:`a_i` are the"
" original ones."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_bogoliubov_uv:8
msgid "A tuple containing the u and v matrices."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_cmatrix:1
msgid "Calculates the correlation matrix."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_cmatrix:3
#, python-brace-format
msgid ""
"The correlation matrix is defined as :math:`C_{ij} = \\langle "
"c_i^\\dagger c_j \\rangle`. This method can also compute out-of-time-"
"ordered correlators (OTOC) by specifying whether to use the current or "
"initial state for the `i` and `j` indices."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_cmatrix:7
msgid "If True, use the current state for the `i` index. Defaults to True."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_cmatrix:9
msgid "If True, use the current state for the `j` index. Defaults to True."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_cmatrix:11
msgid "The correlation matrix."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_cmatrix_majorana:1
msgid "Calculates the correlation matrix in the Majorana basis."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_cmatrix_majorana:3
#, python-brace-format
msgid ""
"The Majorana operators are defined as: :math:`\\gamma_{2i} = c_i + "
"c_i^\\dagger` :math:`\\gamma_{2i+1} = -i(c_i - c_i^\\dagger)`"
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_cmatrix_majorana:7
msgid "The correlation matrix in the Majorana basis."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_covariance_matrix:1
msgid "Calculates the covariance matrix."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_covariance_matrix:3
msgid "The covariance matrix is defined from the Majorana correlation matrix."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_covariance_matrix:5
msgid "The covariance matrix."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_reduced_cmatrix:1
msgid ""
"Calculates the reduced correlation matrix by tracing out specified "
"subsystems."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_reduced_cmatrix:3
msgid ""
"This optimized implementation first slices the alpha matrix, then "
"computes the correlation matrix, reducing complexity from O(L³) to "
"O(L·L_A²) where L_A is the size of the kept subsystem."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.get_reduced_cmatrix:9
msgid "The reduced correlation matrix."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.hopping:1
msgid "Constructs the hopping Hamiltonian between two sites."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.hopping:3
msgid "The hopping Hamiltonian is given by :math:`\\chi c_i^\\dagger c_j + h.c.`."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.hopping:5
msgid "The hopping strength."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.hopping:13
msgid "The hopping Hamiltonian."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.init_alpha:1
msgid "Initializes the alpha matrix for a given set of filled sites."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.init_alpha:3
msgid "A list of site indices that are occupied by fermions."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.init_alpha:7
msgid "The initialized alpha matrix."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.orthogonal:1
msgid "Orthogonalizes the alpha matrix using QR decomposition."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.overlap:1
msgid "overlap upto a U(1) phase"
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.post_select:1
msgid "Post-selects the state based on the occupation of a specific site."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.post_select:3
msgid ""
"This optimized implementation uses vectorized broadcasting operations "
"instead of Python loops, improving JIT compilation performance."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.post_select:6
msgid "The index of the site to post-select on."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.post_select:8
msgid "The desired occupation number (0 or 1). Defaults to 1."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.renyi_entanglement_asymmetry:1
msgid "Computes the Renyi entanglement asymmetry."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.renyi_entanglement_asymmetry:5
msgid "The order of the Renyi entanglement asymmetry."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.renyi_entanglement_asymmetry:9
msgid ""
"The number of samples to use for the Monte Carlo estimation. Defaults to "
"100."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.renyi_entanglement_asymmetry:11
msgid ""
"A tensor of random numbers for the sampling. If None, it will be "
"generated internally. Defaults to None."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.renyi_entanglement_asymmetry:14
msgid ""
"If True, also return the standard deviation of the estimation. Defaults "
"to False."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.renyi_entanglement_asymmetry:16
msgid ""
"The Renyi entanglement asymmetry. If `with_std` is True, a tuple "
"containing the asymmetry and its standard deviation is returned."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.renyi_entropy:1
msgid "Computes the Renyi entropy of order n for a given subsystem."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.renyi_entropy:3
msgid "The order of the Renyi entropy."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.renyi_entropy:5
msgid "A list of site indices to be traced out, defining the subsystem."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.renyi_entropy:7
msgid "The Renyi entropy of order n."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.sc_pairing:1
msgid "Constructs the superconducting pairing Hamiltonian between two sites."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.sc_pairing:3
msgid ""
"The superconducting pairing Hamiltonian is given by :math:`\\chi "
"c_i^\\dagger c_j^\\dagger + h.c.`."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.sc_pairing:5
msgid "The pairing strength."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.sc_pairing:13
msgid "The superconducting pairing Hamiltonian."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.wmatrix:1
msgid "Constructs the transformation matrix W."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.wmatrix:3
msgid "This matrix transforms from the fermionic basis to the Majorana basis."
msgstr ""

#: of tensorcircuit.fgs.FGSSimulator.wmatrix:7
msgid "The transformation matrix W."
msgstr ""

#: of tensorcircuit.fgs.FGSTestSimulator:1
msgid ""
"Never use, only for correctness testing stick to numpy backend and no "
"jit/ad/vmap is available"
msgstr ""

#: ../../source/api/gates.rst:2
msgid "tensorcircuit.gates"
msgstr ""

#: of tensorcircuit.gates:1
msgid ""
"Declarations of single-qubit and two-qubit gates and their corresponding "
"matrix."
msgstr ""

#: of tensorcircuit.gates.Gate:1
msgid "Wrapper of tn.Node, quantum gate"
msgstr ""

#: of tensornetwork.network_components.Node.__init__:1
msgid "Create a node."
msgstr ""

#: of tensornetwork.network_components.Node.__init__:3
msgid ""
"The concrete that is represented by this node, or a `AbstractNode` "
"object. If a tensor is passed, it can be be either a numpy array or the "
"tensor-type of the used backend. If a `AbstractNode` is passed, the "
"passed node has to have the same         backend as given by `backend`."
msgstr ""

#: of tensornetwork.network_components.Node.__init__:7
msgid "Name of the node. Used primarily for debugging."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.add_axis_names:3
#: tensornetwork.network_components.Node.__init__:8
msgid "List of names for each of the tensor's axes."
msgstr ""

#: of tensornetwork.network_components.Node.__init__:9
msgid "The name of the backend or an instance of a `AbstractBackend`."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.add_axis_names:5
#: tensornetwork.network_components.Node.__init__:11
msgid ""
"If there is a repeated name in `axis_names` or if the length     doesn't "
"match the shape of the tensor."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.add_axis_names:1
msgid "Add axis names to a Node."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.add_edge:1
msgid "Add an edge to the node on the given axis."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.add_edge:3
msgid "The edge to add."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.add_edge:4
msgid "The axis the edge points to."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.add_edge:5
msgid "If true, replace the existing edge with the new one."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.add_edge:7
msgid "If the edge on axis is not dangling."
msgstr ""

#: of tensornetwork.network_components.Node.from_serial_dict:1
msgid "Return a node given a serialized dict representing it."
msgstr ""

#: of tensornetwork.network_components.Node.from_serial_dict:3
msgid "A python dict representing a serialized node."
msgstr ""

#: of tensornetwork.network_components.Node.from_serial_dict:5
msgid "A node."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.get_all_dangling:1
msgid "Return the set of dangling edges connected to this node."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.get_all_nondangling:1
msgid "Return the set of nondangling edges connected to this node."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.get_axis_number:1
msgid "Get the axis number for a given axis name or value."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.get_dimension:1
msgid "Get the dimension of the given axis."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.get_dimension:3
msgid "The axis of the underlying tensor."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.get_dimension:5
msgid "The dimension of the given axis."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.get_dimension:7
msgid "if axis isn't an int or if axis is too large or small."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.get_rank:1
msgid "Return rank of tensor represented by self."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.reorder_axes:1
msgid "Reorder axes of the node's tensor."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.reorder_axes:3
msgid "This will also update all of the node's edges."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.reorder_axes:5
msgid "Permutation of the dimensions of the node's tensor."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.reorder_axes:7
#: tensornetwork.network_components.AbstractNode.reorder_edges:9
msgid "This node post reordering."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.reorder_axes:9
#: tensornetwork.network_components.AbstractNode.reorder_edges:12
msgid "If the Node has no tensor."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.reorder_edges:1
msgid "Reorder the edges for this given Node."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.reorder_edges:3
msgid ""
"This will reorder the node's edges and transpose the underlying tensor "
"accordingly."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.reorder_edges:6
msgid "List of edges. The order in the list determines the new edge ordering."
msgstr ""

#: of tensornetwork.network_components.AbstractNode.reorder_edges:11
msgid ""
"If either the list of edges is not the same as expected or     if you try"
" to reorder with a trace edge."
msgstr ""

#: of tensornetwork.network_components.Node.to_serial_dict:1
msgid "Return a serializable dict representing the node."
msgstr ""

#: of tensornetwork.network_components.Node.to_serial_dict:3
msgid "Returns: A dict object."
msgstr ""

#: of tensorcircuit.gates.any_gate:1
msgid "Note one should provide the gate with properly reshaped."
msgstr ""

#: of tensorcircuit.gates.any_gate:3
msgid "corresponding gate"
msgstr ""

#: of tensorcircuit.gates.any_gate:5
msgid "The name of the gate."
msgstr ""

#: of tensorcircuit.gates.any_gate:7
msgid "The dimension of the gate."
msgstr ""

#: of tensorcircuit.gates.any_gate:9
msgid "the resulted gate"
msgstr ""

#: of tensorcircuit.gates.num_to_tensor:1
msgid "Convert the inputs to Tensor with specified dtype."
msgstr ""

#: of tensorcircuit.gates.num_to_tensor:35
msgid "inputs"
msgstr ""

#: of tensorcircuit.gates.num_to_tensor:37
msgid "dtype of the output Tensors"
msgstr ""

#: of tensorcircuit.gates.num_to_tensor:39
msgid "List of Tensors or a single Tensor"
msgstr ""

#: of tensorcircuit.gates.bmatrix:1
msgid "Returns a :math:`\\LaTeX` bmatrix."
msgstr ""

#: of tensorcircuit.gates.bmatrix:13
msgid "Formatted Display:"
msgstr ""

#: of tensorcircuit.gates.bmatrix:15
#, python-brace-format
msgid ""
"\\begin{bmatrix}    1.+0.j & 0.+0.j\\\\    0.+0.j & 1.+0.j \\end{bmatrix}"
"\n"
"\n"
msgstr ""

#: of tensorcircuit.gates.bmatrix:18
msgid "2D numpy array"
msgstr ""

#: of tensorcircuit.gates.bmatrix:20
msgid "ValueError(\"bmatrix can at most display two dimensions\")"
msgstr ""

#: of tensorcircuit.gates.bmatrix:21
msgid ":math:`\\LaTeX`-formatted string for bmatrix of the array a"
msgstr ""

#: of tensorcircuit.gates.cmz_gate:1
msgid ""
"Multi-qubit CCC...Z gate. Decomposed as an MPS of diagonal coefficients "
"connected via CopyNode hyperedges (`chi=2`). Only for memory effciency "
"and large qubit counts, the gain is negative for small qubit count."
msgstr ""

#: of tensorcircuit.gates.cmz_gate:5 tensorcircuit.gates.rzm_gate:7
msgid "The number of qubits the gate applies to."
msgstr ""

#: of tensorcircuit.gates.cmz_gate:7 tensorcircuit.gates.diagonal_gate:5
#: tensorcircuit.gates.rzm_gate:9
msgid "The dimension of the local Hilbert space, defaults to 2."
msgstr ""

#: of tensorcircuit.gates.cmz_gate:9
msgid "Name of the gate, defaults to \"cmz\"."
msgstr ""

#: of tensorcircuit.gates.cmz_gate:11 tensorcircuit.gates.rzm_gate:13
msgid "A QuVector containing the MPS nodes."
msgstr ""

#: of tensorcircuit.gates.cr_gate:1
msgid ""
"Controlled rotation gate. When the control qubit is 1, `rgate` is applied"
" to the target qubit."
msgstr ""

#: of tensorcircuit.gates.cr_gate:3 tensorcircuit.gates.cr_gate:5
#: tensorcircuit.gates.cr_gate:7 tensorcircuit.gates.exponential_gate:12
#: tensorcircuit.gates.exponential_gate_unity:13
#: tensorcircuit.gates.iswap_gate:12 tensorcircuit.gates.r_gate:9
#: tensorcircuit.gates.r_gate:11 tensorcircuit.gates.r_gate:13
#: tensorcircuit.gates.rgate_theoretical:8
#: tensorcircuit.gates.rgate_theoretical:10
#: tensorcircuit.gates.rgate_theoretical:12 tensorcircuit.gates.rx_gate:6
#: tensorcircuit.gates.rxx_gate:13 tensorcircuit.gates.ry_gate:6
#: tensorcircuit.gates.ryy_gate:13 tensorcircuit.gates.rz_gate:6
#: tensorcircuit.gates.rzz_gate:13
msgid "angle in radians"
msgstr ""

#: of tensorcircuit.gates.cr_gate:10
msgid "CR Gate"
msgstr ""

#: of tensorcircuit.gates.diagonal_gate:1
msgid "Apply a diagonal gate as a coefficient node (hyperedge)."
msgstr ""

#: of tensorcircuit.gates.diagonal_gate:3
msgid "The diagonal elements of the gate."
msgstr ""

#: of tensorcircuit.gates.diagonal_gate:7
msgid "Name of the gate, defaults to \"diagonal\"."
msgstr ""

#: of tensorcircuit.gates.diagonal_gate:9
msgid "A Gate containing the diagonal coefficient tensor."
msgstr ""

#: of tensorcircuit.gates.exponential_gate_unity:1
#: tensorcircuit.gates.rxx_gate:1 tensorcircuit.gates.ryy_gate:1
#: tensorcircuit.gates.rzz_gate:1
msgid ""
"Faster exponential gate directly implemented based on RHS. Only works "
"when :math:`U^2 = I` is an identity matrix."
msgstr ""

#: of tensorcircuit.gates.exponential_gate_unity:3
#: tensorcircuit.gates.rxx_gate:3 tensorcircuit.gates.ryy_gate:3
#: tensorcircuit.gates.rzz_gate:3
#, python-brace-format
msgid ""
"\\textrm{exp}(U) &= e^{-j \\theta U} \\\\\n"
"        &= \\cos(\\theta) I - j \\sin(\\theta) U \\\\\n"
"\n"
msgstr ""

#: of tensorcircuit.gates.exponential_gate:6
#: tensorcircuit.gates.exponential_gate_unity:7
#: tensorcircuit.gates.multicontrol_gate:7 tensorcircuit.gates.rxx_gate:7
#: tensorcircuit.gates.ryy_gate:7 tensorcircuit.gates.rzz_gate:7
msgid "input unitary :math:`U`"
msgstr ""

#: of tensorcircuit.gates.exponential_gate:8
#: tensorcircuit.gates.exponential_gate:10
#: tensorcircuit.gates.exponential_gate_unity:9
#: tensorcircuit.gates.exponential_gate_unity:11 tensorcircuit.gates.rxx_gate:9
#: tensorcircuit.gates.rxx_gate:11 tensorcircuit.gates.ryy_gate:9
#: tensorcircuit.gates.ryy_gate:11 tensorcircuit.gates.rzz_gate:9
#: tensorcircuit.gates.rzz_gate:11
msgid "alias for the argument ``unitary``"
msgstr ""

#: of tensorcircuit.gates.exponential_gate_unity:15
#: tensorcircuit.gates.rxx_gate:15 tensorcircuit.gates.ryy_gate:15
#: tensorcircuit.gates.rzz_gate:15
msgid "if True, the angel theta is mutiplied by 1/2, defaults to False"
msgstr ""

#: of tensorcircuit.gates.exponential_gate:14
#: tensorcircuit.gates.exponential_gate_unity:18
#: tensorcircuit.gates.rxx_gate:18 tensorcircuit.gates.ryy_gate:18
#: tensorcircuit.gates.rzz_gate:18
msgid "suffix of Gate name"
msgstr ""

#: of tensorcircuit.gates.exponential_gate:15
#: tensorcircuit.gates.exponential_gate_unity:20
#: tensorcircuit.gates.rxx_gate:20 tensorcircuit.gates.ryy_gate:20
#: tensorcircuit.gates.rzz_gate:20
msgid "Exponential Gate"
msgstr ""

#: of tensorcircuit.gates.exponential_gate:1
msgid "Exponential gate."
msgstr ""

#: of tensorcircuit.gates.exponential_gate:3
#, python-brace-format
msgid ""
"\\textrm{exp}(U) = e^{-j \\theta U}\n"
"\n"
msgstr ""

#: of tensorcircuit.gates.get_u_parameter:1
msgid "From the single qubit unitary to infer three angles of IBMUgate,"
msgstr ""

#: of tensorcircuit.gates.get_u_parameter:3
msgid "numpy array, no backend agnostic version for now"
msgstr ""

#: of tensorcircuit.gates.get_u_parameter:5
msgid "theta, phi, lbd"
msgstr ""

#: of tensorcircuit.gates.iswap_gate:1
msgid "iSwap gate."
msgstr ""

#: of tensorcircuit.gates.iswap_gate:3
#, python-brace-format
msgid ""
"\\textrm{iSwap}(\\theta) =\n"
"\\begin{pmatrix}\n"
"    1 & 0 & 0 & 0\\\\\n"
"    0 & \\cos(\\frac{\\pi}{2} \\theta ) & j \\sin(\\frac{\\pi}{2} \\theta"
" ) & 0\\\\\n"
"    0 & j \\sin(\\frac{\\pi}{2} \\theta ) & \\cos(\\frac{\\pi}{2} \\theta"
" ) & 0\\\\\n"
"    0 & 0 & 0 & 1\\\\\n"
"\\end{pmatrix}\n"
"\n"
msgstr ""

#: of tensorcircuit.gates.iswap_gate:14
msgid "iSwap Gate"
msgstr ""

#: of tensorcircuit.gates.matrix_for_gate:1
msgid "Convert Gate to numpy array."
msgstr ""

#: of tensorcircuit.gates.matrix_for_gate:10
msgid "input Gate"
msgstr ""

#: of tensorcircuit.gates.matrix_for_gate:12
msgid "Corresponding Tensor"
msgstr ""

#: of tensorcircuit.gates.meta_gate:1
msgid ""
"Inner helper function to generate gate functions, such as ``z()`` from "
"``_z_matrix``"
msgstr ""

#: of tensorcircuit.gates.multicontrol_gate:1
msgid ""
"Multicontrol gate. If the control qubits equal to ``ctrl``, :math:`U` is "
"applied to the target qubits."
msgstr ""

#: of tensorcircuit.gates.multicontrol_gate:3
msgid ""
"E.g., ``multicontrol_gate(tc.gates._zz_matrix, [1, 0, 1])`` returns a "
"gate of 5 qubits,"
msgstr ""

#: of tensorcircuit.gates.multicontrol_gate:4
#, python-brace-format
msgid ""
"where the last 2 qubits are applied :math:`ZZ` gate, if the first 3 "
"qubits are :math:`\\ket{101}`."
msgstr ""

#: of tensorcircuit.gates.multicontrol_gate:9
msgid "control bit sequence"
msgstr ""

#: of tensorcircuit.gates.multicontrol_gate:11
msgid "Multicontrol Gate"
msgstr ""

#: of tensorcircuit.gates.phase_gate:1
msgid "The phase gate"
msgstr ""

#: of tensorcircuit.gates.phase_gate:3
#, python-brace-format
msgid ""
"\\textrm{phase}(\\theta) =\n"
"\\begin{pmatrix}\n"
"    1 & 0 \\\\\n"
"    0 & e^{i\\theta} \\\\\n"
"\\end{pmatrix}\n"
"\n"
msgstr ""

#: of tensorcircuit.gates.phase_gate:10
msgid "angle in radians, defaults to 0"
msgstr ""

#: of tensorcircuit.gates.phase_gate:12
msgid "phase gate"
msgstr ""

#: of tensorcircuit.gates.r_gate:1
msgid "General single qubit rotation gate"
msgstr ""

#: of tensorcircuit.gates.r_gate:3
msgid ""
"R(\\theta, \\alpha, \\phi) = \\cos(\\theta) I\n"
"- j \\cos(\\phi) \\sin(\\alpha) \\sin(\\theta) X\n"
"- j \\sin(\\phi) \\sin(\\alpha) \\sin(\\theta) Y\n"
"- j \\sin(\\theta) \\cos(\\alpha) Z\n"
"\n"
msgstr ""

#: of tensorcircuit.gates.r_gate:16
msgid "R Gate"
msgstr ""

#: of tensorcircuit.gates.random_single_qubit_gate:1
msgid "Random single qubit gate described in https://arxiv.org/abs/2002.07730."
msgstr ""

#: of tensorcircuit.gates.random_single_qubit_gate:3
msgid "A random single-qubit gate"
msgstr ""

#: of tensorcircuit.gates.random_two_qubit_gate:1
msgid "Returns a random two-qubit gate."
msgstr ""

#: of tensorcircuit.gates.random_two_qubit_gate:3
msgid "A random two-qubit gate"
msgstr ""

#: of tensorcircuit.gates.rgate_theoretical:1
msgid ""
"Rotation gate implemented by matrix exponential. The output is the same "
"as `rgate`."
msgstr ""

#: of tensorcircuit.gates.rgate_theoretical:3
#, python-brace-format
msgid ""
"R(\\theta, \\alpha, \\phi) = e^{-j \\theta \\left[\\sin(\\alpha) "
"\\cos(\\phi) X\n"
"                                           + \\sin(\\alpha) \\sin(\\phi) "
"Y\n"
"                                           + \\cos(\\alpha) Z\\right]}\n"
"\n"
msgstr ""

#: of tensorcircuit.gates.rgate_theoretical:14
msgid "Rotation Gate"
msgstr ""

#: of tensorcircuit.gates.rx_gate:1
msgid "Rotation gate along :math:`x` axis."
msgstr ""

#: of tensorcircuit.gates.rx_gate:3
msgid ""
"RX(\\theta) = e^{-j\\frac{\\theta}{2}X}\n"
"\n"
msgstr ""

#: of tensorcircuit.gates.rx_gate:8
msgid "RX Gate"
msgstr ""

#: of tensorcircuit.gates.ry_gate:1
msgid "Rotation gate along :math:`y` axis."
msgstr ""

#: of tensorcircuit.gates.ry_gate:3
msgid ""
"RY(\\theta) = e^{-j\\frac{\\theta}{2}Y}\n"
"\n"
msgstr ""

#: of tensorcircuit.gates.ry_gate:8
msgid "RY Gate"
msgstr ""

#: of tensorcircuit.gates.rz_gate:1
msgid "Rotation gate along :math:`z` axis."
msgstr ""

#: of tensorcircuit.gates.rz_gate:3
msgid ""
"RZ(\\theta) = e^{-j\\frac{\\theta}{2}Z}\n"
"\n"
msgstr ""

#: of tensorcircuit.gates.rz_gate:8
msgid "RZ Gate"
msgstr ""

#: of tensorcircuit.gates.rzm_gate:1
msgid ""
"Multi-qubit Z rotation gate `R_zz...z(theta)`. Decomposed as an MPS of "
"diagonal coefficients connected via CopyNode hyperedges (`chi=2`). Only "
"for memory effciency and large qubit counts, the gain is negative for "
"small qubit count."
msgstr ""

#: of tensorcircuit.gates.rzm_gate:5
#: tensorcircuit.quditcircuit.QuditCircuit.rx:5
#: tensorcircuit.quditcircuit.QuditCircuit.ry:5
#: tensorcircuit.quditgates.rxx_matrix_func:8
#: tensorcircuit.quditgates.rzz_matrix_func:10
msgid "Rotation angle."
msgstr ""

#: of tensorcircuit.gates.rzm_gate:11
msgid "Name of the gate, defaults to \"rzm\"."
msgstr ""

#: of tensorcircuit.gates.su4_gate:1
msgid "Two-qubit general SU(4) gate."
msgstr ""

#: of tensorcircuit.gates.su4_gate:3
msgid "the angle tensor (15 components) of the gate."
msgstr ""

#: of tensorcircuit.gates.su4_gate:5
msgid "the name of the gate."
msgstr ""

#: of tensorcircuit.gates.su4_gate:7
msgid "a gate object."
msgstr ""

#: of tensorcircuit.gates.u_gate:1
msgid ""
"IBMQ U gate following the converntion of OpenQASM3.0. See `OpenQASM doc "
"<https://openqasm.com/language/gates.html#built-in-gates>`_"
msgstr ""

#: of tensorcircuit.gates.u_gate:4
#, python-brace-format
msgid ""
"\\begin{split}U(\\theta,\\phi,\\lambda) := \\left(\\begin{array}{cc}\n"
"\\cos(\\theta/2) & -e^{i\\lambda}\\sin(\\theta/2) \\\\\n"
"e^{i\\phi}\\sin(\\theta/2) & e^{i(\\phi+\\lambda)}\\cos(\\theta/2) "
"\\end{array}\\right).\\end{split}"
msgstr ""

#: of tensorcircuit.gates.u_gate:10 tensorcircuit.gates.u_gate:12
#: tensorcircuit.gates.u_gate:14
msgid "_description_, defaults to 0"
msgstr ""

#: ../../source/api/interfaces.rst:2
msgid "tensorcircuit.interfaces"
msgstr ""

#: ../../source/api/interfaces/jax.rst:2
msgid "tensorcircuit.interfaces.jax"
msgstr ""

#: of tensorcircuit.interfaces.jax:1
msgid "Interface wraps quantum function as a jax function"
msgstr ""

#: of tensorcircuit.interfaces.jax.jax_interface:1
msgid "Wrap a function on different ML backend with a jax interface."
msgstr ""

#: of tensorcircuit.interfaces.jax.jax_interface:20
#: tensorcircuit.interfaces.tensorflow.tensorflow_interface:22
#: tensorcircuit.interfaces.torch.torch_interface:28
msgid "The quantum function with tensor in and tensor out"
msgstr ""

#: of tensorcircuit.interfaces.jax.jax_interface:22
#: tensorcircuit.interfaces.tensorflow.tensorflow_interface:26
#: tensorcircuit.interfaces.torch.torch_interface:30
msgid "whether to jit ``fun``, defaults to False"
msgstr ""

#: of tensorcircuit.interfaces.jax.jax_interface:24
#: tensorcircuit.interfaces.tensorflow.tensorflow_interface:28
#: tensorcircuit.interfaces.torch.torch_interface:32
msgid "whether transform tensor backend via dlpack, defaults to False"
msgstr ""

#: of tensorcircuit.interfaces.jax.jax_interface:26
msgid "Optional shape of the function output, defaults to None"
msgstr ""

#: of tensorcircuit.interfaces.jax.jax_interface:28
msgid "Optional dtype of the function output, defaults to None"
msgstr ""

#: of tensorcircuit.interfaces.jax.jax_interface:30
msgid ""
"The same quantum function but now with jax array in and jax array out "
"while AD is also supported"
msgstr ""

#: ../../source/api/interfaces/numpy.rst:2
msgid "tensorcircuit.interfaces.numpy"
msgstr ""

#: of tensorcircuit.interfaces.numpy:1
msgid "Interface wraps quantum function as a numpy function"
msgstr ""

#: of tensorcircuit.interfaces.numpy.numpy_interface:1
msgid "Convert ``fun`` on ML backend into a numpy function"
msgstr ""

#: of tensorcircuit.interfaces.numpy.numpy_interface:23
msgid "The quantum function"
msgstr ""

#: of tensorcircuit.interfaces.numpy.numpy_interface:25
#: tensorcircuit.interfaces.scipy.scipy_optimize_interface:39
msgid "whether to jit ``fun``, defaults to True"
msgstr ""

#: of tensorcircuit.interfaces.numpy.numpy_interface:27
msgid "The numpy interface compatible version of ``fun``"
msgstr ""

#: ../../source/api/interfaces/scipy.rst:2
msgid "tensorcircuit.interfaces.scipy"
msgstr ""

#: of tensorcircuit.interfaces.scipy:1
msgid "Interface wraps quantum function as a scipy function for optimization"
msgstr ""

#: of tensorcircuit.interfaces.scipy.scipy_optimize_interface:1
msgid "Convert ``fun`` into a scipy optimize interface compatible version"
msgstr ""

#: of tensorcircuit.interfaces.scipy.scipy_optimize_interface:35
msgid "The quantum function with scalar out that to be optimized"
msgstr ""

#: of tensorcircuit.interfaces.scipy.scipy_optimize_interface:37
msgid "the shape of parameters that ``fun`` accepts, defaults to None"
msgstr ""

#: of tensorcircuit.interfaces.scipy.scipy_optimize_interface:41
msgid ""
"whether using gradient-based or gradient free scipy optimize interface, "
"defaults to True"
msgstr ""

#: of tensorcircuit.interfaces.scipy.scipy_optimize_interface:44
msgid "The scipy interface compatible version of ``fun``"
msgstr ""

#: ../../source/api/interfaces/tensorflow.rst:2
msgid "tensorcircuit.interfaces.tensorflow"
msgstr ""

#: of tensorcircuit.interfaces.tensorflow:1
msgid "Interface wraps quantum function as a tensorflow function"
msgstr ""

#: of tensorcircuit.interfaces.tensorflow.tensorflow_interface:1
msgid ""
"Wrap a quantum function on different ML backend with a tensorflow "
"interface."
msgstr ""

#: of tensorcircuit.interfaces.tensorflow.tensorflow_interface:24
msgid "output tf dtype or in str"
msgstr ""

#: of tensorcircuit.interfaces.tensorflow.tensorflow_interface:30
#: tensorcircuit.interfaces.torch.torch_interface:34
msgid ""
"The same quantum function but now with torch tensor in and torch tensor "
"out while AD is also supported"
msgstr ""

#: ../../source/api/interfaces/tensortrans.rst:2
msgid "tensorcircuit.interfaces.tensortrans"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans:1
msgid "general function for interfaces transformation"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.args_to_tensor:1
msgid ""
"Function decorator that automatically convert inputs to tensors on "
"current backend"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.args_to_tensor:63
msgid ""
"the wrapped function whose arguments in ``argnums`` position are expected"
" to be tensor format"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.args_to_tensor:66
msgid "position of args under the auto conversion, defaults to 0"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.args_to_tensor:68
msgid ""
"try reshape all input tensor as matrix with shape rank 2, defaults to "
"False"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.args_to_tensor:71
msgid "convert ``Gate`` to tensor, defaults to False"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.args_to_tensor:73
msgid "reshape tensor from ``Gate`` input as matrix, defaults to True"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.args_to_tensor:75
msgid "convert ``QuOperator`` to tensor, defaults to False"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.args_to_tensor:77
msgid "reshape tensor from ``QuOperator`` input as matrix, defaults to True"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.args_to_tensor:79
msgid "whether cast to backend dtype, defaults to True"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.args_to_tensor:81
msgid "The wrapped function"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.general_args_to_numpy:1
msgid "Given a pytree, get the corresponding numpy array pytree"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.general_args_to_numpy:3
msgid "pytree"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.general_args_to_numpy:5
msgid "the same format pytree with all tensor replaced by numpy array"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.numpy_args_to_backend:1
msgid "Given a pytree of numpy arrays, get the corresponding tensor pytree"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.numpy_args_to_backend:3
msgid "pytree of numpy arrays"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.numpy_args_to_backend:5
msgid "str of str of the same pytree shape as args, defaults to None"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.numpy_args_to_backend:7
msgid ""
"str or backend object, defaults to None, indicating the current default "
"backend"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.numpy_args_to_backend:10
msgid ""
"the same format pytree with all numpy array replaced by the tensors in "
"the target backend"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.which_backend:1
msgid "Given a tensor ``a``, return the corresponding backend"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.which_backend:5
msgid ""
"if true, return backend object, if false, return backend str, defaults to"
" True"
msgstr ""

#: of tensorcircuit.interfaces.tensortrans.which_backend:8
msgid "the backend object or backend str"
msgstr ""

#: ../../source/api/interfaces/torch.rst:2
msgid "tensorcircuit.interfaces.torch"
msgstr ""

#: of tensorcircuit.interfaces.torch:1
msgid "Interface wraps quantum function as a torch function"
msgstr ""

#: of tensorcircuit.interfaces.torch.torch_interface:1
msgid "Wrap a quantum function on different ML backend with a pytorch interface."
msgstr ""

#: of tensorcircuit.interfaces.torch.torch_interface_kws:1
msgid ""
"similar to py:meth:`tensorcircuit.interfaces.torch.torch_interface`, but "
"now the interface support static arguments for function ``f``, which is "
"not a tensor and can be used with keyword arguments"
msgstr ""

#: ../../source/api/keras.rst:2
msgid "tensorcircuit.keras"
msgstr ""

#: of tensorcircuit.keras:1
msgid "Keras layer for tc quantum function"
msgstr ""

#: of tensorcircuit.keras.HardwareLayer:1
msgid ""
"Keras Layer wrapping quantum function with cloud qpu access (using "
":py:mod:`tensorcircuit.cloud` module)"
msgstr ""

#: of tensorcircuit.keras.QuantumLayer.__init__:1
msgid ""
"`QuantumLayer` wraps the quantum function `f` as a `keras.Layer` so that "
"tensorcircuit is better integrated with tensorflow. Note that the input "
"of the layer can be tensors or even list/dict of tensors."
msgstr ""

#: of tensorcircuit.keras.QuantumLayer.__init__:5
msgid "Callabel function."
msgstr ""

#: of tensorcircuit.keras.QuantumLayer.__init__:7
msgid "The shape of the weights."
msgstr ""

#: of tensorcircuit.keras.QuantumLayer.__init__:9
msgid "The initializer of the weights, defaults to \"glorot_uniform\""
msgstr ""

#: of tensorcircuit.keras.QuantumLayer.__init__:13
msgid "The regularizer of the weights, defaults to None"
msgstr ""

#: of tensorcircuit.keras.load_func:1
msgid ""
"Load function from the files in the ``tf.savedmodel`` format. We can load"
" several functions at the same time, as they can be the same function of "
"different input shapes."
msgstr ""

#: of tensorcircuit.keras.load_func:24
msgid ""
"The fallback function when all functions loaded are failed, defaults to "
"None"
msgstr ""

#: of tensorcircuit.keras.load_func:26
msgid ""
"When there is not legal loaded function of the input shape and no "
"fallback callable."
msgstr ""

#: of tensorcircuit.keras.load_func:27
msgid ""
"A function that tries all loaded function against the input until the "
"first success one."
msgstr ""

#: of tensorcircuit.keras.output_asis_loss:1
msgid "The keras loss function that directly taking the model output as the loss."
msgstr ""

#: of tensorcircuit.keras.output_asis_loss:3
msgid "Ignoring this parameter."
msgstr ""

#: of tensorcircuit.keras.output_asis_loss:5
msgid "Model output."
msgstr ""

#: of tensorcircuit.keras.output_asis_loss:7
msgid "Model output, which is y_pred."
msgstr ""

#: of tensorcircuit.keras.save_func:1
msgid "Save tf function in the file (``tf.savedmodel`` format)."
msgstr ""

#: of tensorcircuit.keras.save_func:30
msgid "``tf.function`` ed function with graph building"
msgstr ""

#: of tensorcircuit.keras.save_func:32
msgid "the dir path to save the function"
msgstr ""

#: ../../source/api/mps_base.rst:2
msgid "tensorcircuit.mps_base"
msgstr ""

#: of tensorcircuit.mps_base:1
msgid "FiniteMPS from tensornetwork with bug fixed"
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.__init__:1
msgid "Initialize a `FiniteMPS`. If `canonicalize` is `True` the state"
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.__init__:2
msgid ""
"is brought into canonical form, with `BaseMPS.center_position` at "
"`center_position`. if `center_position` is `None` and `canonicalize = "
"True`, `BaseMPS.center_position` is set to 0."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.__init__:6
msgid "A list of `Tensor` objects."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.__init__:7
msgid "The initial position of the center site."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.__init__:8
msgid "If `True` the mps is canonicalized at initialization."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.__init__:9
msgid ""
"The name of the backend that should be used to perform contractions. "
"Available backends are currently 'numpy', 'tensorflow', 'pytorch', 'jax'"
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.apply_one_site_gate:1
msgid ""
"Apply a one-site gate to an MPS. This routine will in general destroy any"
" canonical form of the state. If a canonical form is needed, the user can"
" restore it using `FiniteMPS.position` :param gate: a one-body gate "
":param site: the site where the gate should be applied"
msgstr ""

#: of
#: tensornetwork.matrixproductstates.base_mps.BaseMPS.apply_transfer_operator:1
msgid "Compute the action of the MPS transfer-operator at site `site`."
msgstr ""

#: of
#: tensornetwork.matrixproductstates.base_mps.BaseMPS.apply_transfer_operator:3
msgid "A site of the MPS"
msgstr ""

#: of
#: tensornetwork.matrixproductstates.base_mps.BaseMPS.apply_transfer_operator:4
msgid ""
"* if `1, 'l'` or `'left'`: compute the left-action   of the MPS transfer-"
"operator at `site` on the input `matrix`. * if `-1, 'r'` or `'right'`: "
"compute the right-action   of the MPS transfer-operator at `site` on the "
"input `matrix`"
msgstr ""

#: of
#: tensornetwork.matrixproductstates.base_mps.BaseMPS.apply_transfer_operator:5
msgid ""
"if `1, 'l'` or `'left'`: compute the left-action of the MPS transfer-"
"operator at `site` on the input `matrix`."
msgstr ""

#: of
#: tensornetwork.matrixproductstates.base_mps.BaseMPS.apply_transfer_operator:7
msgid ""
"if `-1, 'r'` or `'right'`: compute the right-action of the MPS transfer-"
"operator at `site` on the input `matrix`"
msgstr ""

#: of
#: tensornetwork.matrixproductstates.base_mps.BaseMPS.apply_transfer_operator:9
msgid "A rank-2 tensor or matrix."
msgstr ""

#: of
#: tensornetwork.matrixproductstates.base_mps.BaseMPS.apply_transfer_operator:11
msgid "The result of applying the MPS transfer-operator to `matrix`"
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.apply_two_site_gate:1
msgid ""
"Apply a two-site gate to an MPS. This routine will in general destroy any"
" canonical form of the state. If a canonical form is needed, the user can"
" restore it using `FiniteMPS.position`."
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.apply_two_site_gate:5
msgid "A two-body gate."
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.apply_two_site_gate:7
msgid "The first site where the gate acts."
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.apply_two_site_gate:9
msgid "The second site where the gate acts."
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.apply_two_site_gate:15
msgid ""
"An optional value to choose the MPS tensor at `center_position` to be "
"isometric after the application of the gate. Defaults to `site1`. If the "
"MPS is canonical (i.e.`BaseMPS.center_position != None`), and if the "
"orthogonality center coincides with either `site1` or `site2`,  the "
"orthogonality center will be shifted to `center_position` (`site1` by "
"default). If the orthogonality center does not coincide with `(site1, "
"site2)` then `MPS.center_position` is set to `None`."
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.apply_two_site_gate:26
#, python-brace-format
msgid ""
"\"rank of gate is {} but has to be 4\", \"site1 = {} is not between 0 <= "
"site < N - 1 = {}\", \"site2 = {} is not between 1 <= site < N = "
"{}\",\"Found site2 ={}, site1={}. Only nearest neighbor gates are "
"currently supported\", \"f center_position = {center_position} not  f in "
"{(site1, site2)} \", or \"center_position = {}, but gate is applied at "
"sites {}, {}. Truncation should only be done if the gate is applied at "
"the center position of the MPS.\""
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.apply_two_site_gate:32
msgid "A scalar tensor containing the truncated weight of the truncation."
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.bond_dimension:1
msgid "The bond dimension of `bond`"
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.bond_dimensions:1
msgid "A list of bond dimensions of `BaseMPS`"
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.canonicalize:1
msgid ""
"Bring the MPS into canonical form according to `center_position`. If "
"`center_position` is `None`, the MPS is canonicalized with "
"`center_position = 0`."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.canonicalize:5
msgid "If `True`, normalize matrices when shifting the orthogonality center."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.canonicalize:8
msgid "The norm of the MPS."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.check_canonical:1
msgid "Check whether the MPS is in the expected canonical form."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.check_canonical:3
msgid "The L2 norm of the vector of local deviations."
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.check_orthonormality:1
msgid "Check orthonormality of tensor at site `site`."
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.check_orthonormality:3
msgid ""
"* if `'l'` or `'left'`: check left orthogonality * if `'r`' or `'right'`:"
" check right orthogonality"
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.check_orthonormality:4
msgid "if `'l'` or `'left'`: check left orthogonality"
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.check_orthonormality:5
msgid "if `'r`' or `'right'`: check right orthogonality"
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.check_orthonormality:6
msgid "The site of the tensor."
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.check_orthonormality:8
msgid "The L2 norm of the deviation from identity."
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.check_orthonormality:9
msgid "scalar `Tensor`"
msgstr ""

#: of
#: tensornetwork.matrixproductstates.base_mps.BaseMPS.check_orthonormality:11
msgid "If which is different from 'l','left', 'r' or 'right'."
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.get_tensor:1
msgid "Returns the `Tensor` object at `site`."
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.get_tensor:3
msgid ""
"If `site==len(self) - 1` `BaseMPS.connector_matrix` is absorbed fromt the"
" right-hand side into the returned `Tensor` object."
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.get_tensor:7
msgid "The site for which to return the `Node`."
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.get_tensor:9
msgid "The tensor at `site`."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.left_envs:1
msgid ""
"Compute left reduced density matrices for site `sites`. This returns a "
"dict `left_envs` mapping sites (int) to Tensors. `left_envs[site]` is the"
" left-reduced density matrix to the left of site `site`."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.left_envs:5
#: tensornetwork.matrixproductstates.finite_mps.FiniteMPS.right_envs:5
msgid "A list of sites of the MPS."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.left_envs:8
msgid "The left-reduced density matrices   at each  site in `sites`."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.left_envs:10
msgid "The left-reduced density matrices"
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.left_envs:11
#: tensornetwork.matrixproductstates.finite_mps.FiniteMPS.right_envs:11
msgid "at each  site in `sites`."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.left_envs:12
#: tensornetwork.matrixproductstates.finite_mps.FiniteMPS.right_envs:12
msgid "`dict` mapping `int` to `Tensor`"
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.measure_local_operator:1
msgid "Measure the expectation value of local operators `ops` site `sites`."
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.measure_local_operator:3
msgid "A list Tensors of rank 2; the local operators to be measured."
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.measure_local_operator:5
msgid "Sites where `ops` act."
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.measure_local_operator:7
msgid "measurements :math:`\\langle` `ops[n]`:math:`\\rangle` for n in `sites`"
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.measure_two_body_correlator:1
msgid ""
"Compute the correlator :math:`\\langle` `op1[site1], "
"op2[s]`:math:`\\rangle` between `site1` and all sites `s` in `sites2`. If"
" `s == site1`, `op2[s]` will be applied first."
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.measure_two_body_correlator:6
msgid "Tensor of rank 2; the local operator at `site1`."
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.measure_two_body_correlator:8
msgid "Tensor of rank 2; the local operator at `sites2`."
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.measure_two_body_correlator:10
msgid "The site where `op1`  acts"
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.measure_two_body_correlator:12
msgid "Sites where operator `op2` acts."
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.measure_two_body_correlator:14
msgid ""
"Correlator :math:`\\langle` `op1[site1], op2[s]`:math:`\\rangle` for `s` "
":math:`\\in` `sites2`."
msgstr ""

#: of tensorcircuit.mps_base.FiniteMPS.physical_dimensions:1
msgid "A list of physical Hilbert-space dimensions of `BaseMPS`"
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.position:1
msgid "Shift `center_position` to `site`."
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.position:3
msgid "The site to which FiniteMPS.center_position should be shifted"
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.position:4
msgid "If `True`, normalize matrices when shifting."
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.position:5
msgid "If not `None`, truncate the MPS bond dimensions to `D`."
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.position:6
msgid ""
"if not `None`, truncate each bond dimension, but keeping the truncation "
"error below `max_truncation_err`."
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.position:9
msgid "The norm of the tensor at `FiniteMPS.center_position`"
msgstr ""

#: of tensornetwork.matrixproductstates.base_mps.BaseMPS.position:12
msgid "If `center_position` is `None`."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.random:1
msgid ""
"Initialize a random `FiniteMPS`. The resulting state is normalized. Its "
"center-position is at 0."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.random:4
msgid "A list of physical dimensions."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.random:5
msgid "A list of bond dimensions."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.random:6
msgid "A numpy dtype."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.random:7
msgid "An optional backend."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.random:9
msgid "`FiniteMPS`"
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.right_envs:1
msgid ""
"Compute right reduced density matrices for site `sites. This returns a "
"dict `right_envs` mapping sites (int) to Tensors. `right_envs[site]` is "
"the right-reduced density matrix to the right of site `site`."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.right_envs:8
msgid "The right-reduced density matrices   at each  site in `sites`."
msgstr ""

#: of tensornetwork.matrixproductstates.finite_mps.FiniteMPS.right_envs:10
msgid "The right-reduced density matrices"
msgstr ""

#: ../../source/api/mpscircuit.rst:2
msgid "tensorcircuit.mpscircuit"
msgstr ""

#: of tensorcircuit.mpscircuit:1
msgid "Quantum circuit: MPS state simulator"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit:1
msgid "``MPSCircuit`` class. Simple usage demo below."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.MPO_to_gate:1
msgid "Convert MPO to gate"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.__init__:1
msgid ""
"MPSCircuit object based on state simulator. Do not use this class with "
"d!=2 directly"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.__init__:8
msgid "The center position of MPS, default to 0"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.__init__:10
msgid ""
"If not None, the initial state of the circuit is taken as ``tensors`` "
"instead of :math:`\\vert 0\\rangle^n` qubits, defaults to None. When "
"``tensors`` are specified, if ``center_position`` is None, then the "
"tensors are canonicalized, otherwise it is assumed the tensors are "
"already canonicalized at the ``center_position``"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.__init__:15
msgid ""
"If not None, it is transformed to the MPS form according to the split "
"rules"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.__init__:17
msgid "alias for the argument ``wavefunction``"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.__init__:19
msgid "Split rules"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_general_gate:1
msgid "Apply a general qubit gate on MPS."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_adjacent_double_gate:3
#: tensorcircuit.mpscircuit.MPSCircuit.apply_double_gate:3
#: tensorcircuit.mpscircuit.MPSCircuit.apply_general_gate:3
msgid "The Gate to be applied"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_general_gate:6
msgid "Qubit indices of the gate"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_general_gate:5
msgid "\"MPS does not support application of gate on > 2 qubits.\""
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_MPO:1
msgid "Apply a Matrix Product Operator (MPO) to the MPS."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_MPO:3
msgid ""
"The application involves three main steps: 1. Contract the MPO tensors "
"with the corresponding MPS tensors. 2. Canonicalize the resulting tensors"
" by moving the orthogonality center. 3. Truncate the bond dimensions to "
"control complexity."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_MPO:8
msgid "A sequence of tensors representing the MPO."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_MPO:10
msgid "The starting index on the MPS where the MPO is applied."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_MPO:12
msgid ""
"If True, the final orthogonality center will be at the left end of the "
"MPO. Otherwise, it will be at the right end. Defaults to True."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_MPO:15
msgid "Truncation options for bond dimension reduction. Defaults to None."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_adjacent_double_gate:1
msgid ""
"Apply a double qubit gate on adjacent qubits of Matrix Product States "
"(MPS)."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_adjacent_double_gate:5
#: tensorcircuit.mpscircuit.MPSCircuit.apply_double_gate:5
msgid "The first qubit index of the gate"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_adjacent_double_gate:7
#: tensorcircuit.mpscircuit.MPSCircuit.apply_double_gate:7
msgid "The second qubit index of the gate"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_adjacent_double_gate:9
msgid "Center position of MPS, default is None"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_double_gate:1
msgid "Apply a double qubit gate on MPS."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_nqubit_gate:1
msgid "Apply an n-qubit gate to the MPS by converting it to an MPO."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_nqubit_gate:3
msgid "The n-qubit gate to apply."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_nqubit_gate:5
msgid "The indices of the qubits to apply the gate to."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_nqubit_gate:7
msgid "Truncation options for the MPO application. Defaults to None."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_single_gate:1
msgid "Apply a single qubit gate on MPS; no truncation is needed."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_single_gate:3
msgid "gate to be applied"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.apply_single_gate:5
msgid "Qubit index of the gate"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.conj:1
msgid "Compute the conjugate of the current MPS."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.conj:3
#: tensorcircuit.mpscircuit.MPSCircuit.copy:3
#: tensorcircuit.mpscircuit.MPSCircuit.copy_without_tensor:3
msgid "The constructed MPS"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.consecutive_swap:1
msgid ""
"Apply a series of SWAP gates to move a qubit from ``index_from`` to "
"``index_to``."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.consecutive_swap:3
msgid "The starting index of the qubit."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.consecutive_swap:5
msgid "The destination index of the qubit."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.consecutive_swap:7
msgid ""
"Truncation options for the SWAP gates. Defaults to None. consistent with "
"the split option of the class."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.copy:1
msgid "Copy the current MPS."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.copy_without_tensor:1
msgid "Copy the current MPS without the tensors."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.expectation:1
msgid "Compute the expectation of corresponding operators in the form of tensor."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.expectation:3
msgid ""
"Operator and its position on the circuit, eg. ``(gates.Z(), [1]), "
"(gates.X(), [2])`` is for operator :math:`Z_1X_2`"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.expectation:9
msgid "If not None, will be used as bra"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.expectation:11
msgid "Whether to conjugate the bra state"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.expectation:13
msgid "Whether to normalize the MPS"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.expectation:15
#: tensorcircuit.mpscircuit.MPSCircuit.set_split_rules:5
#: tensorcircuit.mpscircuit.MPSCircuit.wavefunction_to_tensors:5
msgid "Truncation split"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.expectation:17
msgid "The expectation of corresponding operators"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.gate_to_MPO:1
msgid "Convert gate to MPO form with identities at empty sites"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.get_bond_dimensions:1
msgid "Get the MPS bond dimensions"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.get_bond_dimensions:3
#: tensorcircuit.mpscircuit.MPSCircuit.get_tensors:3
msgid "MPS tensors"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.get_center_position:1
msgid "Get the center position of the MPS"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.get_center_position:3
msgid "center position"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.get_norm:1
msgid "Get the normalized Center Position."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.get_norm:3
msgid "Normalized Center Position."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.get_quvector:1
msgid "Get the representation of the output state in the form of ``QuVector``"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.get_quvector:2
msgid "has to be full contracted in MPS"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.get_tensors:1
msgid "Get the MPS tensors"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.is_valid:1
msgid "Check whether the circuit is legal."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.is_valid:3
msgid "Whether the circuit is legal."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.measure:1
msgid "Take measurement to the given quantum lines."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.mid_measurement:1
msgid ""
"Middle measurement in the z-basis on the circuit, note the wavefunction "
"output is not normalized with ``mid_measurement`` involved, one should "
"normalized the state manually if needed."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.mid_measurement:4
msgid "The index of qubit that the Z direction postselection applied on"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.mid_measurement:6
msgid "0 for spin up, 1 for spin down, defaults to 0"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.normalize:1
msgid "Normalize MPS Circuit according to the center position."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.position:1
msgid "Wrapper of tn.FiniteMPS.position. Set orthogonality center."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.position:4
msgid "The orthogonality center"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.proj_with_mps:1
msgid "Compute the projection between `other` as bra and `self` as ket."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.proj_with_mps:3
msgid "ket of the other MPS, which will be converted to bra automatically"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.proj_with_mps:5
msgid "The projection in form of tensor"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.reduce_dimension:1
msgid "Reduce the bond dimension between two adjacent sites using SVD."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.reduce_dimension:3
msgid "The index of the left tensor of the bond to be truncated."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.reduce_dimension:5
msgid ""
"If True, the orthogonality center will be on the left tensor after "
"truncation. Otherwise, it will be on the right tensor. Defaults to True."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.reduce_dimension:8
msgid "Truncation options for the SVD. Defaults to None."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.reduce_tensor_dimension:1
msgid "Reduce the bond dimension between two general tensors by SVD"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.reduced_density_matrix:1
#: tensorcircuit.u1circuit.U1Circuit.reduced_density_matrix:1
msgid "Compute the reduced density matrix for the specified qubits."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.reduced_density_matrix:3
msgid ""
"The output density matrix indices follow the order given by "
"``subsystem_to_keep``, so passing ``[3, 1]`` yields a different index "
"ordering from ``[1, 3]``."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.reduced_density_matrix:7
#: tensorcircuit.u1circuit.U1Circuit.reduced_density_matrix:3
msgid "The qubits to keep (all others are traced out)."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.reduced_density_matrix:9
#: tensorcircuit.quantum.reduced_density_matrix:10
msgid "The reduced density matrix."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.set_split_rules:1
msgid ""
"Set truncation split when double qubit gates are applied. If nothing is "
"specified, no truncation will take place and the bond dimension will keep"
" growing. For more details, refer to `split_tensor`."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.slice:1
msgid "Get a slice of the MPS (only for internal use)"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.wavefunction:3
msgid "the str indicating the form of the output wavefunction"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.wavefunction:5
msgid "Tensor with shape [1, -1]"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.wavefunction:9
msgid "i--A--B--j  -> i--XX--j"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.wavefunction_to_tensors:1
msgid "Construct the MPS tensors from a given wavefunction."
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.wavefunction_to_tensors:3
msgid "The given wavefunction (any shape is OK)"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.wavefunction_to_tensors:7
msgid "Physical dimension, 2 for MPS and 4 for MPO"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.wavefunction_to_tensors:9
msgid "Whether to normalize the wavefunction"
msgstr ""

#: of tensorcircuit.mpscircuit.MPSCircuit.wavefunction_to_tensors:11
msgid "The tensors"
msgstr ""

#: of tensorcircuit.mpscircuit.split_tensor:1
msgid "Split the tensor by SVD or QR depends on whether a truncation is required."
msgstr ""

#: of tensorcircuit.mpscircuit.split_tensor:3
msgid "The input tensor to split."
msgstr ""

#: of tensorcircuit.mpscircuit.split_tensor:5
msgid "Determine the orthogonal center is on the left tensor or the right tensor."
msgstr ""

#: of tensorcircuit.mpscircuit.split_tensor:7
msgid "Two tensors after splitting"
msgstr ""

#: ../../source/api/noisemodel.rst:2
msgid "tensorcircuit.noisemodel"
msgstr ""

#: of tensorcircuit.noisemodel:1
msgid "General Noise Model Construction."
msgstr ""

#: of tensorcircuit.noisemodel.NoiseConf:1
msgid "``Noise Configuration`` class."
msgstr ""

#: of tensorcircuit.noisemodel.NoiseConf.__init__:1
msgid "Establish a noise configuration."
msgstr ""

#: of tensorcircuit.noisemodel.NoiseConf.add_noise:1
msgid ""
"Add noise channels on specific gates and specific qubits in form of Kraus"
" operators."
msgstr ""

#: of tensorcircuit.noisemodel.NoiseConf.add_noise:3
msgid "noisy gate"
msgstr ""

#: of tensorcircuit.noisemodel.NoiseConf.add_noise:5
msgid "noise channel"
msgstr ""

#: of tensorcircuit.noisemodel.NoiseConf.add_noise:7
msgid ""
"the list of noisy qubit, defaults to None, indicating applying the noise "
"channel on all qubits"
msgstr ""

#: of tensorcircuit.noisemodel.NoiseConf.add_noise_by_condition:1
msgid "Add noise based on specified condition"
msgstr ""

#: of tensorcircuit.noisemodel.NoiseConf.add_noise_by_condition:3
msgid "a function to decide if the noise should be added to the qir."
msgstr ""

#: of tensorcircuit.noisemodel.NoiseConf.add_noise_by_condition:5
msgid "the error channel"
msgstr ""

#: of tensorcircuit.noisemodel.NoiseConf.add_noise_by_condition:7
msgid "the name of the condition. A metadata that does not affect the numerics."
msgstr ""

#: of tensorcircuit.noisemodel.NoiseConf.channel_count:1
msgid "Count the total number of channels in a given circuit"
msgstr ""

#: of tensorcircuit.noisemodel.NoiseConf.channel_count:3
msgid "the circuit to be counted"
msgstr ""

#: of tensorcircuit.noisemodel.NoiseConf.channel_count:5
msgid "the count"
msgstr ""

#: of tensorcircuit.noisemodel.apply_qir_with_noise:1
msgid "A newly defined circuit"
msgstr ""

#: of tensorcircuit.noisemodel.apply_qir_with_noise:3
msgid "The qir of the clean circuit"
msgstr ""

#: of tensorcircuit.noisemodel.apply_qir_with_noise:5
#: tensorcircuit.noisemodel.circuit_with_noise:5
msgid "Noise Configuration"
msgstr ""

#: of tensorcircuit.noisemodel.apply_qir_with_noise:7
#: tensorcircuit.noisemodel.circuit_with_noise:7
msgid "The status for Monte Carlo sampling, defaults to None"
msgstr ""

#: of tensorcircuit.noisemodel.apply_qir_with_noise:9
#: tensorcircuit.noisemodel.circuit_with_noise:9
msgid "A newly constructed circuit with noise"
msgstr ""

#: of tensorcircuit.noisemodel.circuit_with_noise:1
msgid "Noisify a clean circuit."
msgstr ""

#: of tensorcircuit.noisemodel.circuit_with_noise:3
msgid "A clean circuit"
msgstr ""

#: of tensorcircuit.noisemodel.expectation_noisfy:1
msgid "Calculate expectation value with noise configuration."
msgstr ""

#: of tensorcircuit.noisemodel.expectation_noisfy:3
#: tensorcircuit.noisemodel.sample_expectation_ps_noisfy:3
msgid "The clean circuit"
msgstr ""

#: of tensorcircuit.noisemodel.expectation_noisfy:12
msgid "expectation value with noise"
msgstr ""

#: of tensorcircuit.noisemodel.sample_expectation_ps_noisfy:1
msgid "Calculate sample_expectation_ps with noise configuration."
msgstr ""

#: of tensorcircuit.noisemodel.sample_expectation_ps_noisfy:13
msgid ""
"repetition time for Monte Carlo sampling  for noisfy calculation, "
"defaults to 1000"
msgstr ""

#: of tensorcircuit.noisemodel.sample_expectation_ps_noisfy:20
msgid ""
"external randomness given by tensor uniformly from [0, 1], defaults to "
"None, used for measurement sampling"
msgstr ""

#: of tensorcircuit.noisemodel.sample_expectation_ps_noisfy:23
msgid "sample expectation value with noise"
msgstr ""

#: ../../source/api/pauliprop.rst:2
msgid "tensorcircuit.pauliprop"
msgstr ""

#: of tensorcircuit.pauliprop:2
msgid "Pauli Propagation Engine"
msgstr ""

#: of tensorcircuit.pauliprop:4
msgid ""
"This module implements a Pauli Propagation Engine (PPE) that tracks the "
"evolution of Pauli observables through a quantum circuit using a global "
"k-local representation."
msgstr ""

#: of tensorcircuit.pauliprop:7
msgid "Key Features:"
msgstr ""

#: of tensorcircuit.pauliprop:8
msgid ""
"Tracks all k-local Pauli strings globally, avoiding subset boundary "
"issues."
msgstr ""

#: of tensorcircuit.pauliprop:9
msgid "Uses precomputed Neighbor Maps for O(1) fiber lookups."
msgstr ""

#: of tensorcircuit.pauliprop:10
msgid "JAX JIT and AD compatible."
msgstr ""

#: of tensorcircuit.pauliprop:11
msgid "Naturally handles truncation of terms exceeding locality k."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine:1
msgid ""
"A Pauli Propagation Engine that tracks observables in the global k-local "
"space."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine:3
#, python-brace-format
msgid ""
"The state is represented as a flat vector of size $|P_k| = \\sum_{i=0}^k "
"\\binom{N}{i} 3^i$."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine:6
msgid ""
"The engine internally uses single precision (``complex64``) for state "
"coefficients. Since Pauli propagation is an approximate algorithm, the "
"systematic errors introduced by truncation are typically much larger than"
" the numerical errors from single-precision floating point."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.__init__:1
msgid "Initialize the Pauli Propagation Engine."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.__init__:3
msgid "Total number of qubits."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.__init__:5
msgid "Maximum locality of the Pauli strings to track."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.apply_gate:1
#: tensorcircuit.pauliprop.SparsePauliPropagationEngine.apply_gate:1
msgid ""
"Propagate the observable through a quantum gate in the Heisenberg "
"picture. Applies $O \\to U^\\dagger O U$."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.apply_gate:4
msgid "Current observable state vector."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.apply_gate:6
msgid "Name of the gate (e.g., 'rx', 'cnot')."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.apply_gate:8
#: tensorcircuit.pauliprop.SparsePauliPropagationEngine.apply_gate:8
msgid "List of qubit indices the gate acts on."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.apply_gate:10
#: tensorcircuit.pauliprop.SparsePauliPropagationEngine.apply_gate:10
msgid "Optional gate parameters."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.apply_gate:12
msgid "Updated observable state vector."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.compute_expectation_scan:1
#: tensorcircuit.pauliprop.SparsePauliPropagationEngine.compute_expectation_scan:1
msgid "Compute expectation value with JAX scan optimization for layers."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.compute_expectation_scan:3
msgid "Hamiltonian structure array."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.compute_expectation_scan:5
msgid "Hamiltonian weights."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.compute_expectation_scan:7
msgid ""
"A function `f(circuit, params, *extra_inputs)` that defines a circuit "
"layer."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.compute_expectation_scan:9
#: tensorcircuit.pauliprop.SparsePauliPropagationEngine.compute_expectation_scan:9
msgid "Batched parameters for the layers."
msgstr ""

#: of
#: tensorcircuit.pauliprop.PauliPropagationEngine.compute_expectation_scan:11
msgid "Optional static inputs for the layer function."
msgstr ""

#: of
#: tensorcircuit.pauliprop.PauliPropagationEngine.compute_expectation_scan:13
#: tensorcircuit.pauliprop.SparsePauliPropagationEngine.compute_expectation_scan:11
msgid "Final expectation value."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.expectation:1
#: tensorcircuit.pauliprop.SparsePauliPropagationEngine.expectation:1
msgid ""
"Compute the expectation value $\\langle 0| O(t) |0 \\rangle$. Sum "
"coefficients of purely Z observables in the final state."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.expectation:4
msgid "Propagated observable state vector."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.expectation:6
#: tensorcircuit.pauliprop.SparsePauliPropagationEngine.expectation:6
msgid "Real-valued expectation value."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.get_initial_state:1
msgid ""
"Initialize the state vector in the k-local Hilbert space from Hamiltonian"
" terms."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.get_initial_state:3
msgid ""
"Hamiltonian structure array of shape [n_terms, n_qubits]. Each entry is "
"0:I, 1:X, 2:Y, 3:Z."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.get_initial_state:6
msgid "Coefficients for each Hamiltonian term."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.get_initial_state:8
msgid "Initial state vector of size dim+1 (including sink)."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.string_to_code:1
msgid ""
"Convert a Pauli string representation ((qidx, ...), (opcode, ...)) to its"
" index in the basis."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.string_to_code:4
#: tensorcircuit.pauliprop.SparsePauliPropagationEngine.string_to_code:4
msgid "Pauli string as ((qidx, ...), (opcode, ...))."
msgstr ""

#: of tensorcircuit.pauliprop.PauliPropagationEngine.string_to_code:6
msgid "Index in the basis."
msgstr ""

#: of tensorcircuit.pauliprop.SparsePauliPropagationEngine:1
msgid ""
"A Truly Sparse Pauli Propagation Engine that tracks Pauli strings using "
"bitpacked integers. No combinatorial basis is precomputed, making it "
"suitable for hundreds of qubits."
msgstr ""

#: of tensorcircuit.pauliprop.SparsePauliPropagationEngine:6
msgid ""
"The engine internally uses single precision (``complex64``) for "
"coefficients, but utilizes ``int64`` bit-packing for encoding Pauli "
"strings. It is highly recommended to call "
"``tc.set_dtype(\"complex128\")`` when using this engine. While the "
"coefficients remain single-precision, this setting ensures the backend "
"(especially JAX) enables native 64-bit support, which is essential for "
"the ``int64`` encoding and bitwise operations used in the large-scale "
"simulation."
msgstr ""

#: of tensorcircuit.pauliprop.SparsePauliPropagationEngine:15
msgid ""
"The numerical precision error of ``complex64`` is significantly smaller "
"than the systematic approximation error from buffer truncation, making "
"double-precision coefficients practically unnecessary for this engine."
msgstr ""

#: of tensorcircuit.pauliprop.SparsePauliPropagationEngine.apply_gate:4
msgid "Current sparse observable state (codes, weights)."
msgstr ""

#: of tensorcircuit.pauliprop.SparsePauliPropagationEngine.apply_gate:6
msgid "Name of the gate."
msgstr ""

#: of tensorcircuit.pauliprop.SparsePauliPropagationEngine.apply_gate:12
msgid "Updated sparse observable state (codes, weights)."
msgstr ""

#: of
#: tensorcircuit.pauliprop.SparsePauliPropagationEngine.compute_expectation_scan:3
msgid "Initial Hamiltonian structures."
msgstr ""

#: of
#: tensorcircuit.pauliprop.SparsePauliPropagationEngine.compute_expectation_scan:5
msgid "Initial Hamiltonian weights."
msgstr ""

#: of
#: tensorcircuit.pauliprop.SparsePauliPropagationEngine.compute_expectation_scan:7
msgid "A function `f(circuit, params)` that defines a circuit layer."
msgstr ""

#: of tensorcircuit.pauliprop.SparsePauliPropagationEngine.expectation:4
msgid "Propagated sparse observable state (codes, weights)."
msgstr ""

#: of tensorcircuit.pauliprop.SparsePauliPropagationEngine.get_initial_state:1
msgid "Initialize the sparse state (codes, coefficients)."
msgstr ""

#: of tensorcircuit.pauliprop.SparsePauliPropagationEngine.get_initial_state:3
msgid "Hamiltonian structures [n_terms, n_qubits]."
msgstr ""

#: of tensorcircuit.pauliprop.SparsePauliPropagationEngine.get_initial_state:5
msgid "Hamiltonian coefficients."
msgstr ""

#: of tensorcircuit.pauliprop.SparsePauliPropagationEngine.get_initial_state:7
msgid "A tuple of (codes, weights) representing the sparse state."
msgstr ""

#: of tensorcircuit.pauliprop.SparsePauliPropagationEngine.string_to_code:1
msgid ""
"Convert a Pauli string representation ((qidx, ...), (opcode, ...)) to its"
" bit-packed int64 representation."
msgstr ""

#: of tensorcircuit.pauliprop.SparsePauliPropagationEngine.string_to_code:6
msgid "Bit-packed int64 tensor."
msgstr ""

#: of tensorcircuit.pauliprop.pauli_propagation:1
msgid "High-level API for Heisenberg-picture Pauli propagation."
msgstr ""

#: of tensorcircuit.pauliprop.pauli_propagation:3
msgid "The quantum circuit to propagate through."
msgstr ""

#: of tensorcircuit.pauliprop.pauli_propagation:5
msgid ""
"The initial observable. Can be: 1. A list of (coeff, pauli_string) pairs,"
" e.g., ``[(1.0, \"ZZ\"), (-0.5, \"XI\")]``. 2. A structure array if "
"``weights`` is also provided."
msgstr ""

#: of tensorcircuit.pauliprop.pauli_propagation:9
msgid "Optional weights if ``observable`` is a structure array."
msgstr ""

#: of tensorcircuit.pauliprop.pauli_propagation:11
msgid "Maximum locality to track. Defaults to 3."
msgstr ""

#: of tensorcircuit.pauliprop.pauli_propagation:13
msgid "Real-valued expectation value $\\langle 0 | U^\\dagger O U | 0 \\rangle$."
msgstr ""

#: ../../source/api/quantum.rst:2
msgid "tensorcircuit.quantum"
msgstr ""

#: of tensorcircuit.quantum:1
msgid "Quantum state and operator class backend by tensornetwork"
msgstr ""

#: of tensorcircuit.quantum.PauliString2COO:1
msgid "Generate sparse matrix from Pauli string sum"
msgstr ""

#: of tensorcircuit.quantum.PauliString2COO:3
msgid ""
"1D Tensor representing for a Pauli string, e.g. [1, 0, 0, 3, 2] is for "
":math:`X_0Z_3Y_4`"
msgstr ""

#: of tensorcircuit.quantum.PauliString2COO:6
msgid ""
"the weight for the Pauli string defaults to None (all Pauli strings "
"weight 1.0)"
msgstr ""

#: of tensorcircuit.quantum.PauliString2COO:9
msgid "the tensorflow sparse matrix"
msgstr ""

#: of tensorcircuit.quantum.PauliStringSum2COO:1
#: tensorcircuit.quantum.PauliStringSum2COO_numpy:1
msgid ""
"Generate sparse tensor from Pauli string sum. Currently requires "
"tensorflow installed"
msgstr ""

#: of tensorcircuit.quantum.PauliStringSum2COO:4
#: tensorcircuit.quantum.PauliStringSum2COO_numpy:4
#: tensorcircuit.quantum.PauliStringSum2COO_tf:4
#: tensorcircuit.quantum.PauliStringSum2Dense:5
msgid ""
"2D Tensor, each row is for a Pauli string, e.g. [1, 0, 0, 3, 2] is for "
":math:`X_0Z_3Y_4`"
msgstr ""

#: of tensorcircuit.quantum.PauliStringSum2COO:7
#: tensorcircuit.quantum.PauliStringSum2COO_numpy:7
#: tensorcircuit.quantum.PauliStringSum2COO_tf:7
#: tensorcircuit.quantum.PauliStringSum2Dense:8
msgid ""
"1D Tensor, each element corresponds the weight for each Pauli string "
"defaults to None (all Pauli strings weight 1.0)"
msgstr ""

#: of tensorcircuit.quantum.PauliStringSum2COO:10
#: tensorcircuit.quantum.PauliStringSum2COO_numpy:10
#: tensorcircuit.quantum.PauliStringSum2Dense:11
msgid ""
"default False. If True, return numpy coo else return backend compatible "
"sparse tensor"
msgstr ""

#: of tensorcircuit.quantum.PauliStringSum2COO:13
#: tensorcircuit.quantum.PauliStringSum2COO_numpy:13
msgid "the scipy coo sparse matrix"
msgstr ""

#: of tensorcircuit.quantum.PauliStringSum2COO_tf:1
msgid "Generate tensorflow sparse matrix from Pauli string sum [deprecated]"
msgstr ""

#: of tensorcircuit.quantum.PauliStringSum2COO_tf:10
msgid "the tensorflow coo sparse matrix"
msgstr ""

#: of tensorcircuit.quantum.PauliStringSum2Dense:1
msgid ""
"Generate dense matrix from Pauli string sum. Currently requires "
"tensorflow installed."
msgstr ""

#: of tensorcircuit.quantum.PauliStringSum2Dense:14
msgid "the tensorflow dense matrix"
msgstr ""

#: of tensorcircuit.quantum.PauliStringSum2MVP:1
msgid ""
"Generate a matrix-vector product function for a given Pauli string sum. "
"The returned function `mvp(psi)` computes sum(w_i * P_i * psi). This "
"implementation efficiently handles the operation by using backend-"
"agnostic slicing (for X/Y flips) and broadcasting (for Z/Y phases), "
"avoiding explicit matrix construction."
msgstr ""

#: of tensorcircuit.quantum.PauliStringSum2MVP:7
msgid ""
"List of Pauli strings, e.g. [[1, 0, 3], [0, 2, 0]] for X0 Z2 and Y1. (0: "
"I, 1: X, 2: Y, 3: Z)"
msgstr ""

#: of tensorcircuit.quantum.PauliStringSum2MVP:10
msgid "List of weights for each Pauli string."
msgstr ""

#: of tensorcircuit.quantum.PauliStringSum2MVP:12
msgid ""
"MVP function taking wavefunction (shape [2**n] or [2]*n) and returning "
"the same shape."
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector:1
msgid "Represents an adjoint (row) vector via a tensor network."
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.__init__:1
msgid ""
"Constructs a new `QuAdjointVector` from a tensor network. This "
"encapsulates an existing tensor network, interpreting it as an adjoint "
"vector (row vector)."
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.__init__:5
#: tensorcircuit.quantum.QuOperator.__init__:9
msgid "The edges of the network to be used as the input edges."
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.__init__:7
#: tensorcircuit.quantum.QuOperator.__init__:11
#: tensorcircuit.quantum.QuVector.__init__:6
msgid ""
"Nodes used to refer to parts of the tensor network that are not connected"
" to any input or output edges (for example: a scalar factor)."
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.__init__:10
#: tensorcircuit.quantum.QuScalar.__init__:7
#: tensorcircuit.quantum.QuVector.__init__:9
msgid "Optional collection of edges to ignore when performing consistency checks."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.adjoint:1
msgid ""
"The adjoint of the operator. This creates a new `QuOperator` with "
"complex-conjugate copies of all tensors in the network and with the input"
" and output edges switched."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.adjoint:5
msgid "The adjoint of the operator."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.check_network:1
msgid ""
"Check that the network has the expected dimensionality. This checks that "
"all input and output edges are dangling and that there are no other "
"dangling edges (except any specified in `ignore_edges`). If not, an "
"exception is raised."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.contract:1
msgid ""
"Contract the tensor network in place. This modifies the tensor network "
"representation of the operator (or vector, or scalar), reducing it to a "
"single tensor, without changing the value."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.contract:5
msgid "Manually specify the axis ordering of the final tensor."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.contract:7
msgid "The present object."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.copy:1
msgid "The deep copy of the operator."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.copy:3
msgid "The new copy of the operator."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.eval:1
msgid ""
"Contracts the tensor network in place and returns the final tensor. Note "
"that this modifies the tensor network representing the operator. The "
"default ordering for the axes of the final tensor is: `*out_edges, "
"*in_edges`. If there are any \"ignored\" edges, their axes come first: "
"`*ignored_edges, *out_edges, *in_edges`."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.eval:8
#: tensorcircuit.quantum.QuOperator.eval_matrix:6
msgid ""
"Manually specify the axis ordering of the final tensor. The default "
"ordering is determined by `out_edges` and `in_edges` (see above)."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.eval:11
#: tensorcircuit.quantum.QuOperator.eval_matrix:9
#, python-brace-format
msgid "Node count '{}' > 1 after contraction!"
msgstr ""

#: of tensorcircuit.quantum.QuOperator.eval:12
msgid "The final tensor representing the operator."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.eval_matrix:1
msgid ""
"Contracts the tensor network in place and returns the final tensor in two"
" dimentional matrix. The default ordering for the axes of the final "
"tensor is: (:math:`\\prod` dimension of out_edges, :math:`\\prod` "
"dimension of in_edges)"
msgstr ""

#: of tensorcircuit.quantum.QuOperator.eval_matrix:10
msgid "The two-dimentional tensor representing the operator."
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.from_tensor:1
msgid ""
"Construct a `QuAdjointVector` directly from a single tensor. This first "
"wraps the tensor in a `Node`, then constructs the `QuAdjointVector` from "
"that `Node`."
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.from_tensor:27
msgid "The tensor for constructing an QuAdjointVector."
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.from_tensor:29
msgid ""
"Sequence of integer indices specifying the order in which to interpret "
"the axes as subsystems (input edges). If not specified, the axes are "
"taken in ascending order."
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.from_tensor:33
msgid "The new constructed QuAdjointVector give from the given tensor."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.is_adjoint_vector:1
msgid ""
"Returns a bool indicating if QuOperator is an adjoint vector. Examples "
"can be found in the `QuOperator.from_tensor`."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.is_scalar:1
msgid ""
"Returns a bool indicating if QuOperator is a scalar. Examples can be "
"found in the `QuOperator.from_tensor`."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.is_vector:1
msgid ""
"Returns a bool indicating if QuOperator is a vector. Examples can be "
"found in the `QuOperator.from_tensor`."
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.nodes:1
#: tensorcircuit.quantum.QuOperator.nodes:1
#: tensorcircuit.quantum.QuScalar.nodes:1
#: tensorcircuit.quantum.QuVector.nodes:1
msgid "All tensor-network nodes involved in the operator."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.norm:1
msgid ""
"The norm of the operator. This is the 2-norm (also known as the Frobenius"
" or Hilbert-Schmidt norm)."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.partial_trace:1
msgid ""
"The partial trace of the operator. Subsystems to trace out are supplied "
"as indices, so that dangling edges are connected to each other as: "
"`out_edges[i] ^ in_edges[i] for i in subsystems_to_trace_out` This does "
"not modify the original network. The original ordering of the remaining "
"subsystems is maintained."
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.reduced_density:16
#: tensorcircuit.quantum.QuOperator.partial_trace:8
#: tensorcircuit.quantum.QuVector.reduced_density:16
msgid "Indices of subsystems to trace out."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.partial_trace:10
msgid "A new QuOperator or QuScalar representing the result."
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.projector:1
#: tensorcircuit.quantum.QuVector.projector:1
msgid ""
"The projector of the operator. The operator, as a linear operator, on the"
" adjoint of the operator."
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.projector:4
msgid ""
"Set :math:`A` is the operator in matrix form, then the projector of "
"operator is defined as: :math:`A^\\dagger A`"
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.projector:6
#: tensorcircuit.quantum.QuVector.projector:6
msgid "The projector of the operator."
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.reduced_density:1
#: tensorcircuit.quantum.QuVector.reduced_density:1
msgid "The reduced density of the operator."
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.reduced_density:3
#: tensorcircuit.quantum.QuVector.reduced_density:3
msgid ""
"Set :math:`A` is the matrix of the operator, then the reduced density is "
"defined as:"
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.reduced_density:5
#, python-brace-format
msgid "\\mathrm{Tr}_{subsystems}(A^\\dagger A)"
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.reduced_density:9
#: tensorcircuit.quantum.QuVector.reduced_density:9
msgid ""
"Firstly, take the projector of the operator, then trace out the "
"subsystems to trace out are supplied as indices, so that dangling edges "
"are connected to each other as: `out_edges[i] ^ in_edges[i] for i in "
"subsystems_to_trace_out` This does not modify the original network. The "
"original ordering of the remaining subsystems is maintained."
msgstr ""

#: of tensorcircuit.quantum.QuAdjointVector.reduced_density:18
#: tensorcircuit.quantum.QuVector.reduced_density:18
msgid ""
"The QuOperator of the reduced density of the operator with given "
"subsystems."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.tensor_product:1
msgid ""
"Tensor product with another operator. Given two operators `A` and `B`, "
"produces a new operator `AB` representing :math:`A \\otimes B`. The "
"`out_edges` (`in_edges`) of `AB` is simply the concatenation of the "
"`out_edges` (`in_edges`) of `A.copy()` with that of `B.copy()`: "
"`new_out_edges = [*out_edges_A_copy, *out_edges_B_copy]` `new_in_edges = "
"[*in_edges_A_copy, *in_edges_B_copy]`"
msgstr ""

#: of tensorcircuit.quantum.QuOperator.tensor_product:20
msgid "The other operator (`B`)."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.tensor_product:22
msgid "The result (`AB`)."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.trace:1
msgid "The trace of the operator."
msgstr ""

#: of tensorcircuit.quantum.QuOperator:1
msgid ""
"Represents a linear operator via a tensor network. To interpret a tensor "
"network as a linear operator, some of the dangling edges must be "
"designated as `out_edges` (output edges) and the rest as `in_edges` "
"(input edges). Considered as a matrix, the `out_edges` represent the row "
"index and the `in_edges` represent the column index. The (right) action "
"of the operator on another then consists of connecting the `in_edges` of "
"the first operator to the `out_edges` of the second. Can be used to do "
"simple linear algebra with tensor networks."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.__init__:1
msgid ""
"Creates a new `QuOperator` from a tensor network. This encapsulates an "
"existing tensor network, interpreting it as a linear operator. The "
"network is checked for consistency: All dangling edges must either be in "
"`out_edges`, `in_edges`, or `ignore_edges`."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.__init__:7
#: tensorcircuit.quantum.QuVector.__init__:4
msgid "The edges of the network to be used as the output edges."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.__init__:15
msgid ""
"Optional collection of dangling edges to ignore when performing "
"consistency checks."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.__init__:18
msgid ""
"At least one reference node is required to specify a scalar. None "
"provided!"
msgstr ""

#: of tensorcircuit.quantum.QuOperator.from_tensor:1
msgid ""
"Construct a `QuOperator` directly from a single tensor. This first wraps "
"the tensor in a `Node`, then constructs the `QuOperator` from that "
"`Node`."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.from_tensor:28
msgid "The tensor."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.from_tensor:30
msgid "The axis indices of `tensor` to use as `out_edges`."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.from_tensor:32
msgid "The axis indices of `tensor` to use as `in_edges`."
msgstr ""

#: of tensorcircuit.quantum.QuOperator.from_tensor:34
msgid "The new operator."
msgstr ""

#: of tensorcircuit.quantum.QuScalar:1
msgid "Represents a scalar via a tensor network."
msgstr ""

#: of tensorcircuit.quantum.QuScalar.__init__:1
msgid ""
"Constructs a new `QuScalar` from a tensor network. This encapsulates an "
"existing tensor network, interpreting it as a scalar."
msgstr ""

#: of tensorcircuit.quantum.QuScalar.__init__:4
msgid ""
"Nodes used to refer to the tensor network (need not be exhaustive - one "
"node from each disconnected subnetwork is sufficient)."
msgstr ""

#: of tensorcircuit.quantum.QuScalar.from_tensor:1
msgid ""
"Construct a `QuScalar` directly from a single tensor. This first wraps "
"the tensor in a `Node`, then constructs the `QuScalar` from that `Node`."
msgstr ""

#: of tensorcircuit.quantum.QuScalar.from_tensor:22
msgid "The tensor for constructing a new QuScalar."
msgstr ""

#: of tensorcircuit.quantum.QuScalar.from_tensor:24
msgid "The new constructed QuScalar from the given tensor."
msgstr ""

#: of tensorcircuit.quantum.QuVector:1
msgid "Represents a (column) vector via a tensor network."
msgstr ""

#: of tensorcircuit.quantum.QuVector.__init__:1
msgid ""
"Constructs a new `QuVector` from a tensor network. This encapsulates an "
"existing tensor network, interpreting it as a (column) vector."
msgstr ""

#: of tensorcircuit.quantum.QuVector.from_tensor:1
msgid ""
"Construct a `QuVector` directly from a single tensor. This first wraps "
"the tensor in a `Node`, then constructs the `QuVector` from that `Node`."
msgstr ""

#: of tensorcircuit.quantum.QuVector.from_tensor:28
msgid "The tensor for constructing a \"QuVector\"."
msgstr ""

#: of tensorcircuit.quantum.QuVector.from_tensor:30
msgid ""
"Sequence of integer indices specifying the order in which to interpret "
"the axes as subsystems (output edges). If not specified, the axes are "
"taken in ascending order."
msgstr ""

#: of tensorcircuit.quantum.QuVector.from_tensor:34
msgid "The new constructed QuVector from the given tensor."
msgstr ""

#: of tensorcircuit.quantum.QuVector.projector:4
msgid ""
"Set :math:`A` is the operator in matrix form, then the projector of "
"operator is defined as: :math:`A A^\\dagger`"
msgstr ""

#: of tensorcircuit.quantum.QuVector.reduced_density:5
#, python-brace-format
msgid "\\mathrm{Tr}_{subsystems}(A A^\\dagger)"
msgstr ""

#: of tensorcircuit.quantum.check_spaces:1
msgid ""
"Check the vector spaces represented by two lists of edges are compatible."
" The number of edges must be the same and the dimensions of each pair of "
"edges must match. Otherwise, an exception is raised."
msgstr ""

#: of tensorcircuit.quantum.check_spaces:5 tensorcircuit.quantum.check_spaces:7
msgid "List of edges representing a many-body Hilbert space."
msgstr ""

#: of tensorcircuit.quantum.check_spaces:10
#, python-brace-format
msgid ""
"Hilbert-space mismatch: \"Cannot connect {} subsystems with {} "
"subsystems\", or \"Input dimension {} != output dimension {}.\""
msgstr ""

#: of tensorcircuit.quantum.correlation_from_counts:1
msgid ""
"Compute :math:`\\prod_{i\\in \\\\text{index}} s_i`, where the probability"
" for each bitstring is given as a vector ``results``. Results is in the "
"format of \"count_vector\""
msgstr ""

#: of tensorcircuit.quantum.correlation_from_counts:13
#: tensorcircuit.quantum.correlation_from_samples:4
msgid "list of int, indicating the position in the bitstring"
msgstr ""

#: of tensorcircuit.quantum.correlation_from_counts:15
msgid "probability vector of shape 2^n"
msgstr ""

#: of tensorcircuit.quantum.correlation_from_counts:17
msgid "Correlation expectation from measurement shots."
msgstr ""

#: of tensorcircuit.quantum.correlation_from_samples:1
msgid ""
"Compute :math:`\\prod_{i\\in \\\\text{index}} s_i (s=\\pm 1)`, Results is"
" in the format of \"sample_int\" or \"sample_bin\""
msgstr ""

#: of tensorcircuit.quantum.correlation_from_samples:6
msgid "sample tensor"
msgstr ""

#: of tensorcircuit.quantum.correlation_from_samples:10
msgid "Correlation expectation from measurement shots"
msgstr ""

#: of tensorcircuit.quantum.count_d2s:1
msgid ""
"measurement shots results, dense representation to sparse tuple "
"representation non-jittable due to the non fixed return shape count_tuple"
" to count_vector"
msgstr ""

#: of tensorcircuit.quantum.count_d2s:12
msgid "cutoff to determine nonzero elements, defaults to 1e-7"
msgstr ""

#: of tensorcircuit.quantum.count_s2d:1
msgid ""
"measurement shots results, sparse tuple representation to dense "
"representation count_vector to count_tuple"
msgstr ""

#: of tensorcircuit.quantum.count_tuple2dict:1
msgid "count_tuple to count_dict_bin or count_dict_int"
msgstr ""

#: of tensorcircuit.quantum.count_tuple2dict:3
msgid "count_tuple format (indices, counts)"
msgstr ""

#: of tensorcircuit.quantum.count_tuple2dict:5
msgid "number of sites (qubits or qudits)"
msgstr ""

#: of tensorcircuit.quantum.count_tuple2dict:7
#: tensorcircuit.quantum.count_vector2dict:8
msgid "can be \"int\" or \"bin\", defaults to \"bin\""
msgstr ""

#: of tensorcircuit.quantum.count_tuple2dict:9
#: tensorcircuit.quantum.sample_bin2int:7
#: tensorcircuit.quantum.sample_int2bin:7
msgid "local dimension, defaults to 2"
msgstr ""

#: of tensorcircuit.quantum.count_tuple2dict:11
msgid "count_dict"
msgstr ""

#: of tensorcircuit.quantum.count_vector2dict:1
msgid ""
"Convert count_vector to count_dict_bin or count_dict_int. For d>10 cases,"
" a base-d string (0-9A-Z) is used."
msgstr ""

#: of tensorcircuit.quantum.count_vector2dict:4
msgid "tensor in shape [d**n]"
msgstr ""

#: of tensorcircuit.quantum.count_vector2dict:6
#: tensorcircuit.quantum.sample2all:5 tensorcircuit.quantum.sample2count:5
#: tensorcircuit.quantum.sample_bin2int:5
#: tensorcircuit.quantum.sample_int2bin:5
msgid "number of sites"
msgstr ""

#: of tensorcircuit.quantum.count_vector2dict:10
msgid "local dimension (default 2)"
msgstr ""

#: of tensorcircuit.quantum.count_vector2dict:12
msgid "mapping from configuration to count"
msgstr ""

#: of tensorcircuit.quantum.double_state:1
msgid "Compute the double state of the given Hamiltonian operator ``h``."
msgstr ""

#: of tensorcircuit.quantum.double_state:3 tensorcircuit.quantum.gibbs_state:3
#: tensorcircuit.quantum.truncated_free_energy:5
msgid "Hamiltonian operator in form of Tensor."
msgstr ""

#: of tensorcircuit.quantum.double_state:5 tensorcircuit.quantum.free_energy:17
#: tensorcircuit.quantum.gibbs_state:5
#: tensorcircuit.quantum.renyi_free_energy:16
#: tensorcircuit.quantum.truncated_free_energy:7
msgid "Constant for the optimization, default is 1."
msgstr ""

#: of tensorcircuit.quantum.double_state:7
msgid "The double state of ``h`` with the given ``beta``."
msgstr ""

#: of tensorcircuit.quantum.eliminate_identities:1
msgid ""
"Eliminates any connected CopyNodes that are identity matrices. This will "
"modify the network represented by `nodes`. Only identities that are "
"connected to other nodes are eliminated."
msgstr ""

#: of tensorcircuit.quantum.eliminate_identities:5
msgid "Collection of nodes to search."
msgstr ""

#: of tensorcircuit.quantum.eliminate_identities:7
msgid ""
"The Dictionary mapping remaining Nodes to any replacements, Dictionary "
"specifying all dangling-edge replacements."
msgstr ""

#: of tensorcircuit.quantum.entanglement_negativity:1
#: tensorcircuit.quantum.log_negativity:1
#: tensorcircuit.quantum.partial_transpose:1
msgid "_summary_"
msgstr ""

#: of tensorcircuit.quantum.entanglement_negativity:7
#: tensorcircuit.quantum.log_negativity:9
#: tensorcircuit.quantum.partial_transpose:7
#: tensorcircuit.quantum.reduced_density_matrix:14
#: tensorcircuit.quantum.reduced_wavefunction:13
msgid "dimension of qudit system"
msgstr ""

#: of tensorcircuit.quantum.entropy:1
msgid "Compute the entropy from the given density matrix ``rho``."
msgstr ""

#: of tensorcircuit.quantum.entropy:30 tensorcircuit.quantum.free_energy:13
#: tensorcircuit.quantum.renyi_entropy:3
#: tensorcircuit.quantum.renyi_free_energy:12
msgid "The density matrix in form of Tensor or QuOperator."
msgstr ""

#: of tensorcircuit.quantum.entropy:32 tensorcircuit.quantum.free_energy:19
msgid "Epsilon, default is 1e-12."
msgstr ""

#: of tensorcircuit.quantum.entropy:34
msgid "Entropy on the given density matrix."
msgstr ""

#: of tensorcircuit.quantum.extract_tensors_from_qop:1
msgid ""
"Extract and sort tensors from QuOperator for conversion to other tensor "
"network formats."
msgstr ""

#: of tensorcircuit.quantum.extract_tensors_from_qop:3
msgid "Input QuOperator to extract tensors from"
msgstr ""

#: of tensorcircuit.quantum.extract_tensors_from_qop:5
msgid "Tuple containing (sorted_nodes, is_mps, nwires) where:"
msgstr ""

#: of tensorcircuit.quantum.extract_tensors_from_qop:6
msgid "sorted_nodes: List of Node objects sorted in linear chain order"
msgstr ""

#: of tensorcircuit.quantum.extract_tensors_from_qop:7
msgid ""
"is_mps: Boolean flag indicating if the structure is MPS (True) or MPO "
"(False)"
msgstr ""

#: of tensorcircuit.quantum.extract_tensors_from_qop:8
msgid "nwires: Integer number of physical edges/qubits in the system"
msgstr ""

#: of tensorcircuit.quantum.fidelity:1
msgid "Return fidelity scalar between two states rho and rho0."
msgstr ""

#: of tensorcircuit.quantum.fidelity:3
msgid "\\operatorname{Tr}(\\sqrt{\\sqrt{rho} rho_0 \\sqrt{rho}})"
msgstr ""

#: of tensorcircuit.quantum.fidelity:7 tensorcircuit.quantum.fidelity:9
#: tensorcircuit.quantum.mutual_information:3 tensorcircuit.quantum.taylorlnm:3
#: tensorcircuit.quantum.trace_distance:3
#: tensorcircuit.quantum.trace_distance:5
#: tensorcircuit.quantum.truncated_free_energy:3
msgid "The density matrix in form of Tensor."
msgstr ""

#: of tensorcircuit.quantum.fidelity:11
msgid "The sqrtm of a Hermitian matrix ``a``."
msgstr ""

#: of tensorcircuit.quantum.free_energy:1
msgid "Compute the free energy of the given density matrix."
msgstr ""

#: of tensorcircuit.quantum.free_energy:15
#: tensorcircuit.quantum.renyi_free_energy:14
msgid "Hamiltonian operator in form of Tensor or QuOperator."
msgstr ""

#: of tensorcircuit.quantum.free_energy:22
msgid "The free energy of the given density matrix with the Hamiltonian operator."
msgstr ""

#: of tensorcircuit.quantum.generate_local_hamiltonian:1
msgid ""
"Generate a local Hamiltonian operator based on the given sequence of "
"Tensor. Note: further jit is recommended. For large Hilbert space, sparse"
" Hamiltonian is recommended"
msgstr ""

#: of tensorcircuit.quantum.generate_local_hamiltonian:5
msgid "A sequence of Tensor."
msgstr ""

#: of tensorcircuit.quantum.generate_local_hamiltonian:7
msgid "Return Hamiltonian operator in form of matrix, defaults to True."
msgstr ""

#: of tensorcircuit.quantum.generate_local_hamiltonian:9
msgid "The Hamiltonian operator in form of QuOperator or matrix."
msgstr ""

#: of tensorcircuit.quantum.get_all_nodes:1
msgid "Return the set of nodes connected to edges."
msgstr ""

#: of tensorcircuit.quantum.gibbs_state:1
msgid "Compute the Gibbs state of the given Hamiltonian operator ``h``."
msgstr ""

#: of tensorcircuit.quantum.gibbs_state:7
msgid "The Gibbs state of ``h`` with the given ``beta``."
msgstr ""

#: of tensorcircuit.quantum.heisenberg_hamiltonian:1
msgid ""
"Generate Heisenberg Hamiltonian with possible external fields. Currently "
"requires tensorflow installed"
msgstr ""

#: of tensorcircuit.quantum.heisenberg_hamiltonian:13
msgid "input circuit graph"
msgstr ""

#: of tensorcircuit.quantum.heisenberg_hamiltonian:15
msgid "zz coupling, default is 1.0"
msgstr ""

#: of tensorcircuit.quantum.heisenberg_hamiltonian:17
msgid "xx coupling, default is 1.0"
msgstr ""

#: of tensorcircuit.quantum.heisenberg_hamiltonian:19
msgid "yy coupling, default is 1.0"
msgstr ""

#: of tensorcircuit.quantum.heisenberg_hamiltonian:21
msgid "External field on z direction, default is 0.0"
msgstr ""

#: of tensorcircuit.quantum.heisenberg_hamiltonian:23
msgid "External field on y direction, default is 0.0"
msgstr ""

#: of tensorcircuit.quantum.heisenberg_hamiltonian:25
msgid "External field on x direction, default is 0.0"
msgstr ""

#: of tensorcircuit.quantum.heisenberg_hamiltonian:27
msgid "Whether to return sparse Hamiltonian operator, default is True."
msgstr ""

#: of tensorcircuit.quantum.heisenberg_hamiltonian:29
msgid "whether return the matrix in numpy or tensorflow form"
msgstr ""

#: of tensorcircuit.quantum.heisenberg_hamiltonian:32
msgid "Hamiltonian measurements"
msgstr ""

#: of tensorcircuit.quantum.identity:1
msgid ""
"Construct a 'QuOperator' representing the identity on a given space. "
"Internally, this is done by constructing 'CopyNode's for each edge, with "
"dimension according to 'space'."
msgstr ""

#: of tensorcircuit.quantum.identity:26
msgid ""
"A sequence of integers for the dimensions of the tensor product factors "
"of the space (the edges in the tensor network)."
msgstr ""

#: of tensorcircuit.quantum.identity:29
msgid ""
"The data type by np.* (for conversion to dense). defaults None to tc "
"dtype."
msgstr ""

#: of tensorcircuit.quantum.identity:31
msgid "The desired identity operator."
msgstr ""

#: of tensorcircuit.quantum.log_negativity:7
msgid "whether use 2 based log or e based log, defaults to \"e\""
msgstr ""

#: of tensorcircuit.quantum.measurement_counts:1
msgid ""
"Simulate the measuring of each qubit of ``p`` in the computational basis,"
" thus producing output like that of ``qiskit``."
msgstr ""

#: of tensorcircuit.quantum.measurement_counts:4
msgid "Six formats of measurement counts results:"
msgstr ""

#: of tensorcircuit.quantum.measurement_counts:14
#, python-brace-format
msgid ""
"\"count_dict_bin\": # {\"00\": 2, \"01\": 0, \"10\": 0, \"11\": 0} / for "
"cases d\\in [10, 36], \"10\" -> \"A\", ..., \"35\" -> \"Z\""
msgstr ""

#: of tensorcircuit.quantum.measurement_counts:38
msgid ""
"The quantum state, assumed to be normalized, as either a ket or density "
"operator."
msgstr ""

#: of tensorcircuit.quantum.measurement_counts:40
msgid "The number of counts to perform."
msgstr ""

#: of tensorcircuit.quantum.measurement_counts:42
msgid "alias for the argument ``counts``"
msgstr ""

#: of tensorcircuit.quantum.measurement_counts:44
msgid "defaults to be \"direct\", see supported format above"
msgstr ""

#: of tensorcircuit.quantum.measurement_counts:48
msgid ""
"if True, the `state` is directly regarded as a probability list, defaults"
" to be False"
msgstr ""

#: of tensorcircuit.quantum.measurement_counts:56
msgid "if True, jax backend try using a jittable count, defaults to False"
msgstr ""

#: of tensorcircuit.quantum.measurement_counts:58
msgid "The counts for each bit string measured."
msgstr ""

#: of tensorcircuit.quantum.mutual_information:1
msgid "Mutual information between AB subsystem described by ``cut``."
msgstr ""

#: of tensorcircuit.quantum.mutual_information:5
msgid "The AB subsystem."
msgstr ""

#: of tensorcircuit.quantum.mutual_information:7
msgid "The diagonal matrix in form of Tensor."
msgstr ""

#: of tensorcircuit.quantum.mutual_information:9
msgid "The mutual information between AB subsystem described by ``cut``."
msgstr ""

#: of tensorcircuit.quantum.onehot_d_tensor:1
msgid "Construct a one-hot vector (or matrix) of local dimension ``d``."
msgstr ""

#: of tensorcircuit.quantum.onehot_d_tensor:3
msgid "index or indices to set as 1. Can be an int or a backend Tensor."
msgstr ""

#: of tensorcircuit.quantum.onehot_d_tensor:5
msgid "local dimension (number of categories), defaults to 2"
msgstr ""

#: of tensorcircuit.quantum.onehot_d_tensor:7
msgid "one-hot encoded vector (shape [d]) or matrix (shape [len(_k), d])"
msgstr ""

#: of tensorcircuit.quantum.partial_transpose:3
msgid "density matrix"
msgstr ""

#: of tensorcircuit.quantum.partial_transpose:5
msgid "sites int list to be transposed"
msgstr ""

#: of tensorcircuit.quantum.ps2xyz:1
msgid "pauli string list to xyz dict"
msgstr ""

#: of tensorcircuit.quantum.ps2xyz:3
#, python-brace-format
msgid "# ps2xyz([1, 2, 2, 0]) = {\"x\": [0], \"y\": [1, 2], \"z\": []}"
msgstr ""

#: of tensorcircuit.quantum.qop2quimb:1
msgid "Convert QuOperator to MPO or MPS in Quimb package."
msgstr ""

#: of tensorcircuit.quantum.qop2quimb:3
msgid ""
"Requirements: QuOperator must represent valid MPS/MPO structure: - Linear"
" chain topology with open boundaries only - MPS: no input edges, "
"consistent virtual bonds between adjacent tensors - MPO: equal "
"input/output edges, rank-4 tensors - Edge connectivity: each internal "
"node connected to exactly 2 neighbors - Cyclic boundary conditions NOT "
"supported"
msgstr ""

#: of tensorcircuit.quantum.qop2quimb:10 tensorcircuit.quantum.quimb2qop:5
#: tensorcircuit.quantum.tn2qop:5
msgid "MPO in the form of QuOperator"
msgstr ""

#: of tensorcircuit.quantum.qop2quimb:12 tensorcircuit.quantum.quimb2qop:3
msgid "MPO in the form of Quimb package"
msgstr ""

#: of tensorcircuit.quantum.qop2tenpy:1
msgid "Convert TensorCircuit QuOperator to MPO or MPS from TeNPy."
msgstr ""

#: of tensorcircuit.quantum.qop2tenpy:3
msgid ""
"Requirements: QuOperator must represent valid MPS/MPO structure: - Linear"
" chain topology with open boundaries only - MPS: no input edges, "
"consistent virtual bonds, rank-3 or 4(with empty input edges) tensors - "
"MPO: equal input/output edges, rank-4 tensors - Cyclic boundary "
"conditions NOT supported"
msgstr ""

#: of tensorcircuit.quantum.qop2tenpy:9
msgid "The corresponding state/operator as a QuOperator."
msgstr ""

#: of tensorcircuit.quantum.qop2tenpy:11
msgid "MPO or MPS object from the TeNPy package."
msgstr ""

#: of tensorcircuit.quantum.qop2tn:1
msgid "Convert QuOperator back to MPO or MPS in TensorNetwork package."
msgstr ""

#: of tensorcircuit.quantum.qop2tn:3
msgid "MPO or MPS in the form of QuOperator, param in docstring"
msgstr ""

#: of tensorcircuit.quantum.qop2tn:4
msgid "MPO or MPS in the form of TensorNetwork"
msgstr ""

#: of tensorcircuit.quantum.quantum_constructor:1
msgid ""
"Constructs an appropriately specialized QuOperator. If there are no "
"edges, creates a QuScalar. If the are only output (input) edges, creates "
"a QuVector (QuAdjointVector). Otherwise creates a QuOperator."
msgstr ""

#: of tensorcircuit.quantum.quantum_constructor:48
msgid "A list of output edges."
msgstr ""

#: of tensorcircuit.quantum.quantum_constructor:50
msgid "A list of input edges."
msgstr ""

#: of tensorcircuit.quantum.quantum_constructor:52
msgid ""
"Reference nodes for the tensor network (needed if there is a. scalar "
"component)."
msgstr ""

#: of tensorcircuit.quantum.quantum_constructor:55
msgid "Edges to ignore when checking the dimensionality of the tensor network."
msgstr ""

#: of tensorcircuit.quantum.quantum_constructor:58
msgid "The new created QuOperator object."
msgstr ""

#: of tensorcircuit.quantum.quimb2qop:1
msgid "Convert MPO in Quimb package to QuOperator."
msgstr ""

#: of tensorcircuit.quantum.reachable:1
msgid ""
"Computes all nodes reachable from `node` or `edge.node1` by connected "
"edges."
msgstr ""

#: of tensorcircuit.quantum.reachable:3
msgid "A `AbstractNode`/`Edge` or collection of `AbstractNodes`/`Edges`"
msgstr ""

#: of tensorcircuit.quantum.reachable:5
msgid ""
"A list of `AbstractNode` objects that can be reached from `node` via "
"connected edges."
msgstr ""

#: of tensorcircuit.quantum.reachable:8
msgid "If inputs contains other then `Edge` or `Node`."
msgstr ""

#: of tensorcircuit.quantum.reduced_density_matrix:1
msgid "Compute the reduced density matrix from the quantum state ``state``."
msgstr ""

#: of tensorcircuit.quantum.reduced_density_matrix:3
msgid "The quantum state in form of Tensor or QuOperator."
msgstr ""

#: of tensorcircuit.quantum.reduced_density_matrix:5
msgid ""
"the index list that is traced out, if cut is a int, it indicates [0, cut]"
" as the traced out region"
msgstr ""

#: of tensorcircuit.quantum.reduced_density_matrix:8
msgid "probability decoration, default is None."
msgstr ""

#: of tensorcircuit.quantum.reduced_density_matrix:12
msgid "if True, returns a trace 1 density matrix. Otherwise, does not normalize."
msgstr ""

#: of tensorcircuit.quantum.reduced_wavefunction:1
msgid ""
"Compute the reduced wavefunction from the quantum state ``state``. The "
"fixed measure result is guaranteed by users, otherwise final "
"normalization may required in the return"
msgstr ""

#: of tensorcircuit.quantum.reduced_wavefunction:7
msgid "the list of position for qubit to be reduced"
msgstr ""

#: of tensorcircuit.quantum.reduced_wavefunction:9
msgid "the fixed results of given qubits in the same shape list as ``cut``"
msgstr ""

#: of tensorcircuit.quantum.renyi_entropy:1
msgid "Compute the Renyi entropy of order :math:`k` by given density matrix."
msgstr ""

#: of tensorcircuit.quantum.renyi_entropy:5
#: tensorcircuit.quantum.renyi_free_energy:18
msgid "The order of Renyi entropy, default is 2."
msgstr ""

#: of tensorcircuit.quantum.renyi_entropy:7
#: tensorcircuit.quantum.renyi_free_energy:20
msgid "The :math:`k` th order of Renyi entropy."
msgstr ""

#: of tensorcircuit.quantum.renyi_free_energy:1
msgid ""
"Compute the Renyi free energy of the corresponding density matrix and "
"Hamiltonian."
msgstr ""

#: of tensorcircuit.quantum.sample2all:1
msgid ""
"transform ``sample_int`` or ``sample_bin`` results to other forms "
"specified by ``format``"
msgstr ""

#: of tensorcircuit.quantum.sample2all:3
msgid ""
"measurement shots results in ``sample_int`` (shape [shots]) or "
"``sample_bin`` (shape [shots, n])"
msgstr ""

#: of tensorcircuit.quantum.sample2all:7
msgid ""
"see :py:meth:`tensorcircuit.quantum.measurement_results`, defaults to "
"\"count_vector\""
msgstr ""

#: of tensorcircuit.quantum.sample2all:11
msgid "only applicable to count transformation in jax backend, defaults to False"
msgstr ""

#: of tensorcircuit.quantum.sample2all:13
msgid "local dimension (2 for qubit; >2 for qudit), defaults to 2"
msgstr ""

#: of tensorcircuit.quantum.sample2all:15
msgid "measurement results specified as ``format``"
msgstr ""

#: of tensorcircuit.quantum.sample2count:1
msgid "sample_int to count_tuple (indices, counts), size = d**n"
msgstr ""

#: of tensorcircuit.quantum.sample2count:3
msgid "linear-index samples, shape [shots]"
msgstr ""

#: of tensorcircuit.quantum.sample2count:7
msgid "whether to return fixed-size outputs (backend dependent)"
msgstr ""

#: of tensorcircuit.quantum.sample2count:9
msgid "local dimension per site, default 2 (qubit)"
msgstr ""

#: of tensorcircuit.quantum.sample2count:11
msgid "(unique_indices, counts)"
msgstr ""

#: of tensorcircuit.quantum.sample_bin2int:1
msgid "bin sample to int sample"
msgstr ""

#: of tensorcircuit.quantum.sample_bin2int:3
msgid "in shape [trials, n] of elements (0, 1)"
msgstr ""

#: of tensorcircuit.quantum.sample_bin2int:9
msgid "in shape [trials]"
msgstr ""

#: of tensorcircuit.quantum.sample_int2bin:1
msgid "Convert linear-index samples to per-site digits (base-d)."
msgstr ""

#: of tensorcircuit.quantum.sample_int2bin:3
msgid "shape [trials], integers in [0, d**n)"
msgstr ""

#: of tensorcircuit.quantum.sample_int2bin:9
msgid "shape [trials, n], entries in [0, d-1]"
msgstr ""

#: of tensorcircuit.quantum.spin_by_basis:1
msgid ""
"Generate all n-bitstrings as an array, each row is a bitstring basis. "
"Return m-th col."
msgstr ""

#: of tensorcircuit.quantum.spin_by_basis:9
msgid "length of a bitstring"
msgstr ""

#: of tensorcircuit.quantum.spin_by_basis:11
msgid "m<n,"
msgstr ""

#: of tensorcircuit.quantum.spin_by_basis:13
msgid "the binary elements to generate, default is (1, -1)."
msgstr ""

#: of tensorcircuit.quantum.spin_by_basis:15
msgid ""
"The value for the m-th position in bitstring when going through all "
"bitstring basis."
msgstr ""

#: of tensorcircuit.quantum.taylorlnm:1
msgid "Taylor expansion of :math:`ln(x+1)`."
msgstr ""

#: of tensorcircuit.quantum.taylorlnm:5
msgid "The :math:`k` th order, default is 2."
msgstr ""

#: of tensorcircuit.quantum.taylorlnm:7
msgid "The :math:`k` th order of Taylor expansion of :math:`ln(x+1)`."
msgstr ""

#: of tensorcircuit.quantum.tenpy2qop:1
msgid ""
"Converts a TeNPy MPO or MPS to a TensorCircuit QuOperator. This "
"definitive version correctly handles axis ordering and boundary "
"conditions to be compatible with `eval_matrix`."
msgstr ""

#: of tensorcircuit.quantum.tenpy2qop:5
msgid "A MPO or MPS object from the TeNPy package."
msgstr ""

#: of tensorcircuit.quantum.tenpy2qop:7
msgid "The corresponding state or operator as a QuOperator."
msgstr ""

#: of tensorcircuit.quantum.tn2qop:1
msgid "Convert MPO in TensorNetwork package to QuOperator."
msgstr ""

#: of tensorcircuit.quantum.tn2qop:3
msgid "MPO in the form of TensorNetwork package"
msgstr ""

#: of tensorcircuit.quantum.trace_distance:1
msgid ""
"Compute the trace distance between two density matrix ``rho`` and "
"``rho2``."
msgstr ""

#: of tensorcircuit.quantum.trace_distance:7
msgid "Epsilon, defaults to 1e-12"
msgstr ""

#: of tensorcircuit.quantum.trace_distance:9
msgid "The trace distance between two density matrix ``rho`` and ``rho2``."
msgstr ""

#: of tensorcircuit.quantum.trace_product:1
msgid "Compute the trace of several inputs ``o`` as tensor or ``QuOperator``."
msgstr ""

#: of tensorcircuit.quantum.trace_product:3
#, python-brace-format
msgid "\\operatorname{Tr}(\\prod_i O_i)"
msgstr ""

#: of tensorcircuit.quantum.trace_product:22
msgid "The trace of several inputs."
msgstr ""

#: of tensorcircuit.quantum.truncated_free_energy:1
msgid "Compute the truncated free energy from the given density matrix ``rho``."
msgstr ""

#: of tensorcircuit.quantum.truncated_free_energy:9
msgid "The :math:`k` th order, defaults to 2"
msgstr ""

#: of tensorcircuit.quantum.truncated_free_energy:11
msgid "The :math:`k` th order of the truncated free energy."
msgstr ""

#: of tensorcircuit.quantum.u1_enlarge:1
msgid ""
"Enlarge a state s in the subspace with m down spins in n sites to the "
"full Hilbert space wavefunction of size 2**n"
msgstr ""

#: of tensorcircuit.quantum.u1_enlarge:4
msgid "input state of size C_n^m"
msgstr ""

#: of tensorcircuit.quantum.u1_enlarge:6 tensorcircuit.quantum.u1_inds:9
#: tensorcircuit.quantum.u1_mask:4 tensorcircuit.quantum.u1_project:5
msgid "number of total sites"
msgstr ""

#: of tensorcircuit.quantum.u1_enlarge:8 tensorcircuit.quantum.u1_inds:11
#: tensorcircuit.quantum.u1_mask:6 tensorcircuit.quantum.u1_project:7
msgid "number of down spins (1 in 0, 1)"
msgstr ""

#: of tensorcircuit.quantum.u1_enlarge:10
msgid "enlarged state of size 2**n"
msgstr ""

#: of tensorcircuit.quantum.u1_inds:1
msgid "Generate all the combination index of m down spins in n sites."
msgstr ""

#: of tensorcircuit.quantum.u1_inds:13
msgid "index tensor"
msgstr ""

#: of tensorcircuit.quantum.u1_mask:1
msgid ""
"Return the 1d array of size 2**n filled with zero, one only in elements "
"corresponding to the m down spins"
msgstr ""

#: of tensorcircuit.quantum.u1_project:1
msgid "Project a state s to the subspace with m down spins in n sites"
msgstr ""

#: of tensorcircuit.quantum.u1_project:3
msgid "input state of size 2**n"
msgstr ""

#: of tensorcircuit.quantum.u1_project:9
msgid "projected state of size C_n^m"
msgstr ""

#: of tensorcircuit.quantum.xyz2ps:1
msgid "xyz dict to pauli string list"
msgstr ""

#: ../../source/api/quditcircuit.rst:2
msgid "tensorcircuit.quditcircuit"
msgstr ""

#: of tensorcircuit.quditcircuit:1
msgid "Quantum circuit: state simulator for **qudits** (d-level systems)."
msgstr ""

#: of tensorcircuit.quditcircuit:3
msgid ""
"This module provides a high-level `QuditCircuit` API that mirrors "
"`tensorcircuit.circuit.Circuit` but targets qudits with dimension `3 <= d"
" <= 36`. For string-encoded samples/counts, digits use `0-9A-Z` where "
"`A=10, ..., Z=35`."
msgstr ""

#: of tensorcircuit.quditcircuit:8
msgid "For qubits (`d=2`) please use :class:`tensorcircuit.circuit.Circuit`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit:1
msgid ""
"The `QuditCircuit` class provides a d-dimensional state-vector simulator "
"and a thin wrapper around :class:`tensorcircuit.circuit.Circuit`, "
"exposing a qudit-friendly API and docstrings."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit:4
msgid "**Quick example (d=3):**"
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit:13
msgid ""
"For `3 <= d <= 36`, string samples and count keys use base-`d` characters"
" `0-9A-Z` (`A=10, ..., Z=35`)."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit:16
msgid "Number of qudits (wires) in the circuit."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit:18
msgid "Qudit local dimension `d`. Must satisfy `3 <= d <= 36`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit:20
msgid "Optional initial state as a wavefunction."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit:22
msgid "Optional initial state in MPS/MPO-like form."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit:24
msgid ""
"Internal contraction/splitting configuration passed through to "
":class:`~tensorcircuit.circuit.Circuit`."
msgstr ""

#: ../../source/api/quditcircuit.rst
msgid "Variables"
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit:28
msgid "Whether the simulator is a density-matrix simulator (`False` here)."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit:30
msgid "Property for the local dimension `d`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit:32
msgid "Property for the number of qudits."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.csum:1
msgid "Apply a generalized controlled-sum gate `CSUM` (a.k.a. qudit CNOT)."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.cphase:3
#: tensorcircuit.quditcircuit.QuditCircuit.csum:3
msgid "Two qudit indices `(control, target)`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.cphase:5
#: tensorcircuit.quditcircuit.QuditCircuit.csum:5
msgid "Optional control value. If `None`, defaults to `1`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.cphase:7
#: tensorcircuit.quditcircuit.QuditCircuit.csum:7
msgid "If arity is not two."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.cphase:1
msgid "Apply a controlled-phase gate `CPHASE`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.h:1
msgid ""
"Apply the generalized Hadamard-like gate `H` (DFT on `d` levels) on the "
"given qudit."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.h:3
#: tensorcircuit.quditcircuit.QuditCircuit.i:3
#: tensorcircuit.quditcircuit.QuditCircuit.rx:3
#: tensorcircuit.quditcircuit.QuditCircuit.ry:3
#: tensorcircuit.quditcircuit.QuditCircuit.rz:3
#: tensorcircuit.quditcircuit.QuditCircuit.u8:3
#: tensorcircuit.quditcircuit.QuditCircuit.x:3
#: tensorcircuit.quditcircuit.QuditCircuit.z:3
msgid "Qudit index."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.i:1
msgid "Apply the generalized identity gate `I` on the given qudit."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rx:1
msgid "Single-qudit rotation `RX` on a selected two-level subspace `(j, k)`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rx:7
#: tensorcircuit.quditcircuit.QuditCircuit.ry:7
msgid "Source level of the rotation subspace (0-based)."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rx:9
#: tensorcircuit.quditcircuit.QuditCircuit.ry:9
msgid "Target level of the rotation subspace (0-based)."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rx:11
#: tensorcircuit.quditcircuit.QuditCircuit.ry:11
msgid "If `j == k` or indices are outside `[0, d-1]`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rxx:1
msgid "Two-qudit interaction `RXX` acting on subspaces `(j1, k1)` and `(j2, k2)`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rxx:3
#: tensorcircuit.quditcircuit.QuditCircuit.rzz:3
msgid "Two qudit indices `(q1, q2)`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rxx:5
#: tensorcircuit.quditcircuit.QuditCircuit.rzz:5
msgid "Interaction angle."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rxx:7
#: tensorcircuit.quditcircuit.QuditCircuit.rzz:7
msgid "Source level of the first qudit subspace."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rxx:9
#: tensorcircuit.quditcircuit.QuditCircuit.rzz:9
msgid "Target level of the first qudit subspace."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rxx:11
#: tensorcircuit.quditcircuit.QuditCircuit.rzz:11
msgid "Source level of the second qudit subspace."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rxx:13
#: tensorcircuit.quditcircuit.QuditCircuit.rzz:13
msgid "Target level of the second qudit subspace."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rxx:15
#: tensorcircuit.quditcircuit.QuditCircuit.rzz:15
msgid "If levels are invalid or the arity is not two."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.ry:1
msgid "Single-qudit rotation `RY` on a selected two-level subspace `(j, k)`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rz:1
msgid "Single-qudit phase rotation `RZ` applied on level `j`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rz:5
msgid "Phase rotation angle around Z."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rz:7
msgid "Level where the phase is applied (0-based)."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rz:9
msgid "If `j` is outside `[0, d-1]`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.rzz:1
msgid "Two-qudit interaction `RZZ` acting on subspaces `(j1, k1)` and `(j2, k2)`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.u8:1
msgid "Apply the parameterized single-qudit gate `U8`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.u8:5
msgid "Gate parameter `gamma`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.u8:7
msgid "Gate parameter `z`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.u8:9
msgid "Gate parameter `eps`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.x:1
msgid "Apply the generalized shift gate `X` on the given qudit (adds `+1 mod d`)."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.z:1
msgid ""
"Apply the generalized phase gate `Z` on the given qudit (multiplies by "
"`omega^k`)."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.amplitude:1
msgid "Return the amplitude for a given base-`d` string `l`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.amplitude:3
#, python-brace-format
msgid ""
"For state simulators, this computes :math:`\\langle l \\vert \\psi "
"\\rangle`. For density-matrix simulators, it would compute "
":math:`\\operatorname{Tr}(\\rho \\vert l \\rangle \\langle l \\vert)` "
"(note the square in magnitude differs between the two formalisms)."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.amplitude:7
#: tensorcircuit.quditcircuit.QuditCircuit.replace_mps_inputs:3
msgid "**Example**"
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.amplitude_before:1
msgid ""
"Return the (uncontracted) tensor network nodes for the amplitude of "
"configuration `l`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.amplitude_before:3
#, python-brace-format
msgid ""
"For state simulators, this corresponds to :math:`\\langle l \\vert \\psi "
"\\rangle`. For density-matrix simulators, it would correspond to "
":math:`\\operatorname{Tr}(\\rho \\vert l \\rangle \\langle l \\vert)`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.amplitude_before:7
msgid "Base-`d` string using `0-9A-Z` or an equivalent tensor index."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.any:1
msgid "Apply an arbitrary unitary on one or two qudits."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.any:3
msgid "Target qudit indices."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.any:5
msgid ""
"Unitary matrix acting on the specified qudit(s), with shape `(d, d)` or "
"`(d^2, d^2)`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.any:7
msgid "Optional label stored in the circuit history."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.dim:1
msgid "dimension of the qudit circuit"
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.expectation_before:1
msgid "Build (but do not contract) the tensor network for expectation evaluation."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.mid_measurement:1
msgid "Mid-circuit Z-basis post-selection."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.mid_measurement:3
msgid "The returned state is **not normalized**; normalize manually if needed."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.mid_measurement:5
msgid "Qudit index where post-selection is applied."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.mid_measurement:7
#, python-brace-format
msgid "Post-selected digit in `{0, ..., d-1}`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.mid_measurement:9
msgid "Unnormalized post-selected state."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.nqudits:1
msgid "qudit number of the circuit"
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.probability:1
msgid "Get the length-`d^n` probability vector over the computational basis."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.projected_subsystem:1
msgid ""
"remaining wavefunction or density matrix on sites in `left`, while fixing"
" the other sites to given digits as indicated by `traceout`."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.projected_subsystem:4
msgid "A tensor encoding digits (0..d-1) for sites to be fixed; jittable."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.projected_subsystem:6
msgid "Tuple of site indices to keep (non-jittable argument)."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.projected_subsystem:8
msgid "Remaining wavefunction or density matrix on the kept sites."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.replace_mps_inputs:1
msgid ""
"Replace the input state (keeps circuit structure) using an MPS/MPO-like "
"representation."
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.sample:9
#, python-brace-format
msgid ""
"Output format. See :py:meth:`tensorcircuit.quantum.measurement_results`."
"        Supported formats for qudits include:  \"count_vector\": # "
"np.array([2, 0, 0, 0])  \"count_dict_bin\": # {\"00\": 2, \"01\": 0, "
"\"10\": 0, \"11\": 0}     for cases :math:`d\\in [11, 36]`, use 0-9A-Z "
"digits (e.g., 'A' -> 10, ..., 'Z' -> 35);"
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.sample:10
msgid "Supported formats for qudits include:"
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.sample:15
msgid ""
"for cases :math:`d\\in [11, 36]`, use 0-9A-Z digits (e.g., 'A' -> 10, "
"..., 'Z' -> 35);"
msgstr ""

#: of tensorcircuit.quditcircuit.QuditCircuit.sample:32
msgid "For integer-based output formats not suitable for qudits."
msgstr ""

#: ../../source/api/quditgates.rst:2
msgid "tensorcircuit.quditgates"
msgstr ""

#: of tensorcircuit.quditgates:1
msgid "Single-qudit and two-qudit gates and their matrix representations."
msgstr ""

#: of tensorcircuit.quditgates:3
msgid ""
"This module implements gates for **qudits** (d-level systems), providing "
"d-dimensional analogues of familiar qubit gates as well as qudit-specific"
" primitives."
msgstr ""

#: of tensorcircuit.quditgates:6
msgid ""
"**Registry** Single-qudit builders: ``I``, ``X``, ``Z``, ``H``, ``RX``, "
"``RY``, ``RZ``, ``U8``. Two-qudit builders: ``RXX``, ``RZZ``, ``CPHASE``,"
" ``CSUM``."
msgstr ""

#: of tensorcircuit.quditgates:10
msgid ""
"These names are used by higher-level APIs (e.g. "
":class:`tensorcircuit.quditcircuit.QuditCircuit`)."
msgstr ""

#: of tensorcircuit.quditgates.cphase_matrix_func:1
msgid "Qudit controlled-phase (``CPHASE``) gate."
msgstr ""

#: of tensorcircuit.quditgates.cphase_matrix_func:3
#: tensorcircuit.quditgates.csum_matrix_func:3
msgid "Logical definition:"
msgstr ""

#: of tensorcircuit.quditgates.cphase_matrix_func:5
#, python-brace-format
msgid ""
"\\lvert r \\rangle \\lvert s \\rangle \\;\\longmapsto\\;\n"
"\\omega^{rs} \\lvert r \\rangle \\lvert s \\rangle"
msgstr ""

#: of tensorcircuit.quditgates.cphase_matrix_func:10
#: tensorcircuit.quditgates.csum_matrix_func:10
msgid "Matrix form:"
msgstr ""

#: of tensorcircuit.quditgates.cphase_matrix_func:12
#, python-brace-format
msgid ""
"\\mathrm{SUMZ}_d =\n"
"\\begin{bmatrix}\n"
"    I_d & 0   & 0   & \\cdots & 0 \\\\\n"
"    0   & Z_d & 0   & \\cdots & 0 \\\\\n"
"    0   & 0   & Z_d^2 & \\cdots & 0 \\\\\n"
"    \\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\\n"
"    0   & 0   & 0   & \\cdots & Z_d^{d-1}\n"
"\\end{bmatrix}"
msgstr ""

#: of tensorcircuit.quditgates.cphase_matrix_func:23
#: tensorcircuit.quditgates.csum_matrix_func:23
#: tensorcircuit.quditgates.swap_matrix_func:5
msgid "Qudit dimension (for each register)."
msgstr ""

#: of tensorcircuit.quditgates.cphase_matrix_func:25
msgid ""
"Optional control value in ``[0, d-1]``. If ``None``, builds the full SUMZ"
" block-diagonal."
msgstr ""

#: of tensorcircuit.quditgates.cphase_matrix_func:27
msgid "Optional primitive ``d``-th root of unity for ``Z_d``."
msgstr ""

#: of tensorcircuit.quditgates.cphase_matrix_func:29
msgid "``(d*d, d*d)`` matrix representing the controlled-phase."
msgstr ""

#: of tensorcircuit.quditgates.cphase_matrix_func:31
#: tensorcircuit.quditgates.csum_matrix_func:29
msgid "If ``cv`` is provided and is outside ``[0, d-1]``."
msgstr ""

#: of tensorcircuit.quditgates.csum_matrix_func:1
msgid "Qudit controlled-sum (``CSUM`` / ``SUMX``) gate."
msgstr ""

#: of tensorcircuit.quditgates.csum_matrix_func:5
msgid ""
"\\lvert r \\rangle \\lvert s \\rangle \\;\\longmapsto\\;\n"
"\\lvert r \\rangle \\lvert r+s \\pmod d \\rangle"
msgstr ""

#: of tensorcircuit.quditgates.csum_matrix_func:12
#, python-brace-format
msgid ""
"\\mathrm{SUMX}_d =\n"
"\\begin{bmatrix}\n"
"    I_d & 0   & 0   & \\cdots & 0 \\\\\n"
"    0   & X_d & 0   & \\cdots & 0 \\\\\n"
"    0   & 0   & X_d^2 & \\cdots & 0 \\\\\n"
"    \\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\\n"
"    0   & 0   & 0   & \\cdots & X_d^{d-1}\n"
"\\end{bmatrix}"
msgstr ""

#: of tensorcircuit.quditgates.csum_matrix_func:25
msgid ""
"Optional control value in ``[0, d-1]``. If ``None``, builds the full SUMX"
" block-diagonal."
msgstr ""

#: of tensorcircuit.quditgates.csum_matrix_func:27
msgid "``(d*d, d*d)`` matrix representing the controlled-sum."
msgstr ""

#: of tensorcircuit.quditgates.h_matrix_func:1
msgid "Discrete Fourier transform (Hadamard-like) on ``d`` levels."
msgstr ""

#: of tensorcircuit.quditgates.h_matrix_func:3
msgid ""
"H_d\\lvert j \\rangle = \\frac{1}{\\sqrt{d}} \\sum_{k=0}^{d-1} "
"\\omega^{jk}\\lvert k \\rangle\n"
"\n"
msgstr ""

#: of tensorcircuit.quditgates.h_matrix_func:5
#: tensorcircuit.quditgates.i_matrix_func:3
#: tensorcircuit.quditgates.rx_matrix_func:11
#: tensorcircuit.quditgates.ry_matrix_func:11
#: tensorcircuit.quditgates.rz_matrix_func:11
#: tensorcircuit.quditgates.s_matrix_func:5
#: tensorcircuit.quditgates.x_matrix_func:5
#: tensorcircuit.quditgates.z_matrix_func:5
msgid "Qudit dimension."
msgstr ""

#: of tensorcircuit.quditgates.h_matrix_func:7
#: tensorcircuit.quditgates.s_matrix_func:7
#: tensorcircuit.quditgates.z_matrix_func:7
#, python-brace-format
msgid ""
"Optional primitive ``d``-th root of unity. Defaults to :math:`e^{2\\pi "
"i/d}`."
msgstr ""

#: of tensorcircuit.quditgates.h_matrix_func:9
msgid "``(d, d)`` matrix for :math:`H_d`."
msgstr ""

#: of tensorcircuit.quditgates.i_matrix_func:1
msgid "Identity matrix of size ``d``."
msgstr ""

#: of tensorcircuit.quditgates.i_matrix_func:5
msgid "``(d, d)`` identity matrix."
msgstr ""

#: of tensorcircuit.quditgates.rx_matrix_func:1
msgid "Rotation-X (``RX``) on a selected two-level subspace of a qudit."
msgstr ""

#: of tensorcircuit.quditgates.rx_matrix_func:3
#, python-brace-format
msgid ""
"Acts like the qubit :math:`RX(\\theta)` on levels :math:`j` and "
":math:`k`, identity elsewhere. On the :math:`\\{\\lvert j\\rangle,\\lvert"
" k\\rangle\\}` subspace the matrix equals"
msgstr ""

#: of tensorcircuit.quditgates.rx_matrix_func:6
#, python-brace-format
msgid ""
"RX(\\theta) = \\cos\\tfrac{\\theta}{2}\\,(\\lvert j\\rangle\\!\\langle "
"j\\rvert+\\lvert k\\rangle\\!\\langle k\\rvert)\n"
"- i\\,\\sin\\tfrac{\\theta}{2}\\,(\\lvert j\\rangle\\!\\langle k\\rvert +"
" \\lvert k\\rangle\\!\\langle j\\rvert)\n"
"+ I.\n"
"\n"
msgstr ""

#: of tensorcircuit.quditgates.rx_matrix_func:13
#: tensorcircuit.quditgates.ry_matrix_func:13
#: tensorcircuit.quditgates.rz_matrix_func:13
msgid "Rotation angle :math:`\\theta`."
msgstr ""

#: of tensorcircuit.quditgates.rx_matrix_func:15
#: tensorcircuit.quditgates.ry_matrix_func:15
msgid "First level index."
msgstr ""

#: of tensorcircuit.quditgates.rx_matrix_func:17
#: tensorcircuit.quditgates.ry_matrix_func:17
msgid "Second level index."
msgstr ""

#: of tensorcircuit.quditgates.rx_matrix_func:19
msgid "``(d, d)`` matrix for :math:`RX(\\theta)` on the :math:`j,k` subspace."
msgstr ""

#: of tensorcircuit.quditgates.rxx_matrix_func:1
msgid "Two-qudit ``RXX(\\theta)`` on a selected two-state subspace."
msgstr ""

#: of tensorcircuit.quditgates.rxx_matrix_func:3
msgid ""
"Acts like a qubit :math:`RXX` on the subspace spanned by :math:`\\lvert "
"j1, j2\\rangle` and :math:`\\lvert k1, k2\\rangle`."
msgstr ""

#: of tensorcircuit.quditgates.rxx_matrix_func:6
#: tensorcircuit.quditgates.rzz_matrix_func:8
msgid "Dimension of each qudit (assumed equal)."
msgstr ""

#: of tensorcircuit.quditgates.rxx_matrix_func:10
#: tensorcircuit.quditgates.rxx_matrix_func:12
msgid "Level on qudit-1."
msgstr ""

#: of tensorcircuit.quditgates.rxx_matrix_func:14
#: tensorcircuit.quditgates.rxx_matrix_func:16
msgid "Level on qudit-2."
msgstr ""

#: of tensorcircuit.quditgates.rxx_matrix_func:18
msgid ""
"``(d*d, d*d)`` matrix representing :math:`RXX(\\theta)` on the selected "
"subspace."
msgstr ""

#: of tensorcircuit.quditgates.ry_matrix_func:1
msgid "Rotation-Y (``RY``) on a selected two-level subspace of a qudit."
msgstr ""

#: of tensorcircuit.quditgates.ry_matrix_func:3
#, python-brace-format
msgid ""
"Acts like the qubit :math:`RY(\\theta)` on levels :math:`j` and "
":math:`k`, identity elsewhere. On the :math:`\\{\\lvert j\\rangle,\\lvert"
" k\\rangle\\}` subspace the matrix equals"
msgstr ""

#: of tensorcircuit.quditgates.ry_matrix_func:6
#, python-brace-format
msgid ""
"RY(\\theta) = \\cos\\tfrac{\\theta}{2}\\,(\\lvert j\\rangle\\!\\langle "
"j\\rvert+\\lvert k\\rangle\\!\\langle k\\rvert)\n"
"+ \\sin\\tfrac{\\theta}{2}\\,(\\lvert k\\rangle\\!\\langle j\\rvert - "
"\\lvert j\\rangle\\!\\langle k\\rvert)\n"
"+ I.\n"
"\n"
msgstr ""

#: of tensorcircuit.quditgates.ry_matrix_func:19
msgid "``(d, d)`` matrix for :math:`RY(\\theta)` on the :math:`j,k` subspace."
msgstr ""

#: of tensorcircuit.quditgates.rz_matrix_func:1
msgid "Rotation-Z (``RZ``) on a selected level of a qudit."
msgstr ""

#: of tensorcircuit.quditgates.rz_matrix_func:3
msgid ""
"Acts like the qubit :math:`RZ(\\theta)` but applies a phase only to level"
" :math:`j`. On the computational basis it equals"
msgstr ""

#: of tensorcircuit.quditgates.rz_matrix_func:6
#, python-brace-format
msgid ""
"RZ(\\theta) = I + (e^{i\\theta}-1)\\,\\lvert j\\rangle\\!\\langle "
"j\\rvert,\n"
"\n"
msgstr ""

#: of tensorcircuit.quditgates.rz_matrix_func:9
#, python-brace-format
msgid ""
"i.e. :math:`\\lvert j\\rangle \\mapsto e^{i\\theta}\\,\\lvert j\\rangle` "
"and all other levels unchanged."
msgstr ""

#: of tensorcircuit.quditgates.rz_matrix_func:15
msgid "Level index receiving the phase."
msgstr ""

#: of tensorcircuit.quditgates.rz_matrix_func:17
msgid ""
"``(d, d)`` diagonal matrix implementing :math:`RZ(\\theta)` on level "
":math:`j`."
msgstr ""

#: of tensorcircuit.quditgates.rzz_matrix_func:1
msgid "Two-qudit ``RZZ(\\theta)`` on a selected two-state subspace."
msgstr ""

#: of tensorcircuit.quditgates.rzz_matrix_func:3
#, python-brace-format
msgid ""
"Acts like a qubit "
":math:`RZZ(\\theta)=\\exp(-i\\,\\tfrac{\\theta}{2}\\,\\sigma_z)` on the "
"two-dimensional subspace spanned by :math:`\\lvert j1, j2\\rangle` and "
":math:`\\lvert k1, k2\\rangle`, and as identity elsewhere. The resulting "
"block is diagonal with phases :math:`\\mathrm{diag}(e^{-i\\theta/2},\\, "
"e^{+i\\theta/2})`."
msgstr ""

#: of tensorcircuit.quditgates.rzz_matrix_func:12
msgid "Level on qudit-1 for the first basis state."
msgstr ""

#: of tensorcircuit.quditgates.rzz_matrix_func:14
msgid "Level on qudit-1 for the second basis state."
msgstr ""

#: of tensorcircuit.quditgates.rzz_matrix_func:16
msgid "Level on qudit-2 for the first basis state."
msgstr ""

#: of tensorcircuit.quditgates.rzz_matrix_func:18
msgid "Level on qudit-2 for the second basis state."
msgstr ""

#: of tensorcircuit.quditgates.rzz_matrix_func:20
msgid "``(d*d, d*d)`` matrix representing subspace :math:`RZZ(\\theta)`."
msgstr ""

#: of tensorcircuit.quditgates.rzz_matrix_func:22
msgid "If indices are out of range or select the same basis state."
msgstr ""

#: of tensorcircuit.quditgates.s_matrix_func:1
msgid "Diagonal phase gate ``S_d`` on ``d`` levels."
msgstr ""

#: of tensorcircuit.quditgates.s_matrix_func:3
#, python-brace-format
msgid ""
"S_d\\lvert j \\rangle = \\omega^{j(j+p_d)/2}\\lvert j \\rangle,\\quad p_d"
" = (d \\bmod 2)\n"
"\n"
msgstr ""

#: of tensorcircuit.quditgates.s_matrix_func:9
msgid "``(d, d)`` diagonal matrix for :math:`S_d`."
msgstr ""

#: of tensorcircuit.quditgates.swap_matrix_func:1
msgid "SWAP gate for two qudits of equal dimension :math:`d`."
msgstr ""

#: of tensorcircuit.quditgates.swap_matrix_func:3
msgid ""
"Exchanges basis states :math:`\\lvert i\\rangle\\lvert j\\rangle \\to "
"\\lvert j\\rangle\\lvert i\\rangle`."
msgstr ""

#: of tensorcircuit.quditgates.swap_matrix_func:7
msgid "``(d^2, d^2)`` permutation matrix implementing SWAP."
msgstr ""

#: of tensorcircuit.quditgates.u8_matrix_func:1
msgid "``U8`` diagonal single-qudit gate for prime dimensions."
msgstr ""

#: of tensorcircuit.quditgates.u8_matrix_func:3
msgid ""
"See ref: Howard, Mark, and Jiri Vala. \"Qudit versions of the qubit "
"\\pi/8 gate.\" Physical Review A 86, no. 2 (2012): 022316. "
"https://doi.org/10.1103/PhysRevA.86.022316"
msgstr ""

#: of tensorcircuit.quditgates.u8_matrix_func:7
msgid ""
"This gate is the qudit analogue of the qubit :math:`\\pi/8` gate, defined"
" in *Howard & Campbell, Phys. Rev. A 86, 022316 (2012)*. It is diagonal "
"in the computational basis with exponents determined by modular "
"polynomials in the parameters :math:`\\gamma, z, \\epsilon`. These gates,"
" together with Pauli and Clifford operations, generate the full single-"
"qudit Clifford hierarchy."
msgstr ""

#: of tensorcircuit.quditgates.u8_matrix_func:13
msgid ""
"For :math:`d=2`, this reduces (up to global phase) to the standard qubit "
":math:`\\pi/8` gate."
msgstr ""

#: of tensorcircuit.quditgates.u8_matrix_func:15
#, python-brace-format
msgid ""
"For :math:`d=3`, the exponents live in :math:`\\mathbb{Z}_9` and the "
"primitive ninth root :math:`\\zeta = e^{2\\pi i/9}` is used."
msgstr ""

#: of tensorcircuit.quditgates.u8_matrix_func:17
#, python-brace-format
msgid ""
"For prime :math:`d>3`, the construction uses the modular inverse of 12 in"
" :math:`\\mathbb{Z}_d`."
msgstr ""

#: of tensorcircuit.quditgates.u8_matrix_func:20
msgid "Qudit dimension (must be prime)."
msgstr ""

#: of tensorcircuit.quditgates.u8_matrix_func:22
#, python-brace-format
msgid ""
"Shear parameter :math:`\\gamma' \\in \\mathbb{Z}_d`. If :math:`gamma = "
"0`, the gate is a diagonal Clifford. If :math:`gamma \\neq 0`, the gate "
"is a genuine non-Clifford (analogue of :math:`\\pi/8`)."
msgstr ""

#: of tensorcircuit.quditgates.u8_matrix_func:26
#, python-brace-format
msgid ""
"Displacement parameter :math:`z' \\in \\mathbb{Z}_d`, which sets the "
"symplectic part of the associated Clifford."
msgstr ""

#: of tensorcircuit.quditgates.u8_matrix_func:29
#, python-brace-format
msgid ""
"Phase offset parameter :math:`\\epsilon' \\in \\mathbb{Z}_d`. It only "
"contributes a global phase factor :math:`\\omega^{\\epsilon'}`."
msgstr ""

#: of tensorcircuit.quditgates.u8_matrix_func:32
#, python-brace-format
msgid ""
"Optional primitive :math:`d`-th root of unity (complex). Defaults to "
":math:`e^{2\\pi i/d}` for d>3, and :math:`e^{2\\pi i/9}` for d=3."
msgstr ""

#: of tensorcircuit.quditgates.u8_matrix_func:35
msgid "``(d, d)`` diagonal matrix of dtype ``npdtype``."
msgstr ""

#: of tensorcircuit.quditgates.u8_matrix_func:37
msgid ""
"If ``d`` is not prime; if 12 has no modular inverse mod ``d`` (for "
"``d>3``); or if the computed exponents do not sum to 0 mod ``d`` (or 0 "
"mod 3 for ``d=3``)."
msgstr ""

#: of tensorcircuit.quditgates.x_matrix_func:1
msgid "Generalized Pauli-X on a ``d``-level system."
msgstr ""

#: of tensorcircuit.quditgates.x_matrix_func:3
msgid ""
"X_d\\lvert j \\rangle = \\lvert (j+1) \\bmod d \\rangle\n"
"\n"
msgstr ""

#: of tensorcircuit.quditgates.x_matrix_func:7
msgid "``(d, d)`` matrix for :math:`X_d`."
msgstr ""

#: of tensorcircuit.quditgates.z_matrix_func:1
msgid "Generalized Pauli-Z on a ``d``-level system."
msgstr ""

#: of tensorcircuit.quditgates.z_matrix_func:3
#, python-brace-format
msgid ""
"Z_d\\lvert j \\rangle = \\omega^{j}\\lvert j \\rangle,\\quad "
"\\omega=e^{2\\pi i/d}\n"
"\n"
msgstr ""

#: of tensorcircuit.quditgates.z_matrix_func:9
msgid "``(d, d)`` matrix for :math:`Z_d`."
msgstr ""

#: ../../source/api/results.rst:2
msgid "tensorcircuit.results"
msgstr ""

#: ../../source/api/results/counts.rst:2
msgid "tensorcircuit.results.counts"
msgstr ""

#: of tensorcircuit.results.counts:1
msgid "dict related functionalities"
msgstr ""

#: of tensorcircuit.results.counts.count2vec:1
msgid ""
"Convert a dictionary of counts (with string keys) to a probability/count "
"vector."
msgstr ""

#: of tensorcircuit.results.counts.count2vec:3
msgid "Support:"
msgstr ""

#: of tensorcircuit.results.counts.count2vec:4
msgid ""
"base-d string (d <= 36), characters taken from 0-9A-Z (case-insensitive) "
"For example:"
msgstr ""

#: of tensorcircuit.results.counts.count2vec:6
msgid ""
"qubit: '0101' qudit: '012' or '09A' (A represents 10, which means [0, 9, "
"10])"
msgstr ""

#: of tensorcircuit.results.counts.count2vec:9
#: tensorcircuit.results.counts.marginal_count:3
#: tensorcircuit.results.counts.normalized_count:3
#: tensorcircuit.results.counts.reverse_count:3
#: tensorcircuit.results.counts.sort_count:3
msgid "A dictionary mapping bit strings to counts"
msgstr ""

#: of tensorcircuit.results.counts.count2vec:11
msgid "Whether to normalize the counts to probabilities, defaults to True"
msgstr ""

#: of tensorcircuit.results.counts.count2vec:13
#: tensorcircuit.results.counts.vec2count:6
msgid "Dimensionality of the vector, defaults to 2"
msgstr ""

#: of tensorcircuit.results.counts.count2vec:15
msgid "Probability vector as numpy array"
msgstr ""

#: of tensorcircuit.results.counts.expectation:1
msgid ""
"compute diagonal operator expectation value from bit string count "
"dictionary"
msgstr ""

#: of tensorcircuit.results.counts.expectation:3
msgid "count dict for bitstring histogram"
msgstr ""

#: of tensorcircuit.results.counts.expectation:5
#: tensorcircuit.results.readout_mitigation.ReadoutMit.expectation:5
msgid ""
"if defaults as None, then ``diagonal_op`` must be set a list of qubit "
"that we measure Z op on"
msgstr ""

#: of tensorcircuit.results.counts.expectation:8
#: tensorcircuit.results.readout_mitigation.ReadoutMit.expectation:8
msgid ""
"shape [n, 2], explicitly indicate the diagonal op on each qubit eg. [1, "
"-1] for z [1, 1] for I, etc."
msgstr ""

#: of tensorcircuit.results.counts.expectation:11
msgid "the expectation value"
msgstr ""

#: of tensorcircuit.results.counts.kl_divergence:1
msgid "Compute the Kullback-Leibler divergence between two count distributions."
msgstr ""

#: of tensorcircuit.results.counts.kl_divergence:3
msgid "First count dictionary"
msgstr ""

#: of tensorcircuit.results.counts.kl_divergence:5
msgid "Second count dictionary"
msgstr ""

#: of tensorcircuit.results.counts.kl_divergence:7
msgid "KL divergence value"
msgstr ""

#: of tensorcircuit.results.counts.marginal_count:1
msgid ""
"Compute the marginal distribution of a count dictionary over specified "
"qubits."
msgstr ""

#: of tensorcircuit.results.counts.marginal_count:5
msgid "List of qubit indices to keep in the marginal distribution"
msgstr ""

#: of tensorcircuit.results.counts.marginal_count:7
msgid "A new count dictionary with marginal distribution"
msgstr ""

#: of tensorcircuit.results.counts.merge_count:1
msgid "Merge multiple count dictionaries by summing up their counts"
msgstr ""

#: of tensorcircuit.results.counts.merge_count:3
msgid "Variable number of count dictionaries"
msgstr ""

#: of tensorcircuit.results.counts.merge_count:5
msgid "Merged count dictionary"
msgstr ""

#: of tensorcircuit.results.counts.normalized_count:1
msgid "Normalize the count dictionary to represent probabilities."
msgstr ""

#: of tensorcircuit.results.counts.normalized_count:5
msgid "A new dictionary with probabilities instead of counts"
msgstr ""

#: of tensorcircuit.results.counts.plot_histogram:1
msgid ""
"See ``qiskit.visualization.plot_histogram``: "
"https://qiskit.org/documentation/stubs/qiskit.visualization.plot_histogram.html"
msgstr ""

#: of tensorcircuit.results.counts.plot_histogram:4
msgid "interesting kw options include: ``number_to_keep`` (int)"
msgstr ""

#: of tensorcircuit.results.counts.reverse_count:1
msgid "Reverse the bit string keys in a count dictionary."
msgstr ""

#: of tensorcircuit.results.counts.reverse_count:5
msgid "A new dictionary with reversed bit string keys"
msgstr ""

#: of tensorcircuit.results.counts.sort_count:1
msgid "Sort the count dictionary by counts in descending order."
msgstr ""

#: of tensorcircuit.results.counts.sort_count:5
msgid "A new dictionary sorted by count values (descending)"
msgstr ""

#: of tensorcircuit.results.counts.vec2count:1
msgid ""
"Map a count/probability vector of length D to a dictionary with base-d "
"string keys (0-9A-Z). Only generate string keys when d <= 36; if d is "
"inferred to be > 36, raise a NotImplementedError."
msgstr ""

#: of tensorcircuit.results.counts.vec2count:4
msgid "A one-dimensional vector of length D = d**n"
msgstr ""

#: of tensorcircuit.results.counts.vec2count:5
msgid "Whether to prune near-zero elements (threshold 1e-8)"
msgstr ""

#: of tensorcircuit.results.counts.vec2count:7
#, python-brace-format
msgid "{base-d string key: value}, key length n"
msgstr ""

#: ../../source/api/results/qem.rst:2
msgid "tensorcircuit.results.qem"
msgstr ""

#: ../../source/api/results/qem/benchmark_circuits.rst:2
msgid "tensorcircuit.results.qem.benchmark_circuits"
msgstr ""

#: of tensorcircuit.results.qem.benchmark_circuits:1
msgid "circuits for quantum chip benchmark"
msgstr ""

#: ../../source/api/results/qem/qem_methods.rst:2
msgid "tensorcircuit.results.qem.qem_methods"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods:1
msgid "quantum error mitigation functionalities"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.add_dd:1
msgid "Add DD sequence to A circuit"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.add_dd:3
#: tensorcircuit.results.qem.qem_methods.prune_ddcircuit:4
msgid "circuit"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.add_dd:5
msgid "The rule to conduct the DD sequence"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.add_dd:7
#: tensorcircuit.results.qem.qem_methods.prune_ddcircuit:8
msgid "new circuit"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_dd:1
msgid "Apply dynamic decoupling (DD) and return the mitigated results."
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_dd:4
#: tensorcircuit.results.qem.qem_methods.apply_zne:3
msgid "The aim circuit."
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_dd:6
#: tensorcircuit.results.qem.qem_methods.apply_rc:5
msgid "A executor that executes a circuit and return results."
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_dd:8
msgid ""
"The rule to construct DD sequence, can use default rule "
"\"dd_option.rules.xx\""
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_dd:9
#, python-brace-format
msgid ""
"or custom rule \"['X','X']\" :type rule: Union[Callable[[int], Any], "
"List[str]] :param rule_args:An optional dictionary of keyword arguments "
"for ``rule``, defaults to {}. :type rule_args: Dict[str, Any], optional "
":param num_trials: The number of independent experiments to average over,"
" defaults to 1 :type num_trials: int, optional :param full_output: If "
"``False`` only the mitigated expectation value is"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_dd:16
msgid ""
"returned. If ``True`` a dictionary containing all DD data is returned "
"too, defaults to False"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_dd:19
msgid "ignore the DD sequences that added to unused qubits, defaults to True"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_dd:21
msgid "dd sequence full fill the idle circuits, defaults to False"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_dd:23
#: tensorcircuit.results.qem.qem_methods.apply_rc:11
msgid "whether the output is bit string, defaults to False"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_dd:25
msgid ""
"mitigated expectation value or mitigated expectation value and DD circuit"
" information"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_rc:1
msgid "Apply Randomized Compiling or Pauli twirling on two-qubit gates."
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_rc:3
msgid "Input circuit"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_rc:7
msgid "Number of circuits for RC, defaults to 1"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_rc:9
msgid ""
"Whether simplify the circuits by merging single qubit gates, defaults to "
"True"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_rc:13
msgid "Mitigated results by RC"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_zne:1
msgid "Apply zero-noise extrapolation (ZNE) and return the mitigated results."
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_zne:5
msgid ""
"A executor that executes a single circuit or a batch of circuits and "
"return results."
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_zne:7
msgid "Determines the extropolation method."
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_zne:9
msgid "The scaling function for the aim circuit, defaults to fold_gates_at_random"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_zne:11
msgid ""
"Number of times expectation values are computed by the executor, average "
"each point, defaults to 1."
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.apply_zne:14
msgid "Mitigated average value by ZNE."
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.prune_ddcircuit:1
msgid ""
"Discard DD sequence on idle qubits and Discard identity gate (no "
"identity/idle gate on device now) filled in DD sequence."
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.prune_ddcircuit:6
msgid "qubit list to apply DD sequence"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.used_qubits:1
msgid "Create a qubit list that includes all qubits having gate manipulation."
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.used_qubits:3
msgid "a circuit"
msgstr ""

#: of tensorcircuit.results.qem.qem_methods.used_qubits:5
msgid "qubit list"
msgstr ""

#: ../../source/api/results/readout_mitigation.rst:2
msgid "tensorcircuit.results.readout_mitigation"
msgstr ""

#: of tensorcircuit.results.readout_mitigation:1
msgid "readout error mitigation functionalities"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.__init__:1
msgid "The Class for readout error mitigation"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.__init__:3
msgid "execute function to run the cirucit"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.__init__:5
msgid "iteration threshold, defaults to 4096"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.apply_correction:1
msgid "Main readout mitigation program for all methods."
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.apply_correction:3
msgid "raw count"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.apply_correction:5
#: tensorcircuit.results.readout_mitigation.ReadoutMit.mapping_preprocess:6
msgid "user-defined logical qubits to show final mitted results"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.apply_correction:7
#: tensorcircuit.results.readout_mitigation.ReadoutMit.expectation:11
#: tensorcircuit.results.readout_mitigation.ReadoutMit.mapping_preprocess:8
msgid "positional_logical_mapping, defaults to None."
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.apply_correction:9
#: tensorcircuit.results.readout_mitigation.ReadoutMit.expectation:13
#: tensorcircuit.results.readout_mitigation.ReadoutMit.mapping_preprocess:10
msgid "logical_physical_mapping, defaults to None"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.apply_correction:11
msgid "defaults to None"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.apply_correction:13
msgid "mitigation method, defaults to \"square\""
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.apply_correction:15
msgid "defaults to 25"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.apply_correction:17
msgid "defaults to 1e-5"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.apply_correction:19
msgid ""
":param return_mitigation_overhead:defaults to False :type "
"return_mitigation_overhead: bool, optional :param details: defaults to "
"False :type details: bool, optional :return: mitigated count :rtype: ct"
msgstr ""

#: of
#: tensorcircuit.results.readout_mitigation.ReadoutMit.apply_readout_mitigation:1
msgid "Main readout mitigation program for method=\"inverse\" or \"square\""
msgstr ""

#: of
#: tensorcircuit.results.readout_mitigation.ReadoutMit.apply_readout_mitigation:3
msgid "the raw count"
msgstr ""

#: of
#: tensorcircuit.results.readout_mitigation.ReadoutMit.apply_readout_mitigation:5
msgid "mitigation method, defaults to \"inverse\""
msgstr ""

#: of
#: tensorcircuit.results.readout_mitigation.ReadoutMit.apply_readout_mitigation:7
msgid "mitigated count"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.cals_from_api:1
msgid "Get local calibriation matrix from cloud API from tc supported providers"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.cals_from_api:3
msgid "list of physical qubits to be calibriated"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.cals_from_api:5
msgid "the device str to qurey for the info, defaults to None"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.cals_from_system:1
msgid "Get calibrattion information from system."
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.cals_from_system:3
msgid "calibration qubit list (physical qubits on device)"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.cals_from_system:5
msgid "shots used for runing the circuit, defaults to 8192"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.cals_from_system:7
msgid "calibration method, defaults to \"local\", it can also be \"global\""
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.expectation:1
msgid "Calculate expectation value after readout error mitigation"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.expectation:3
msgid "raw counts"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.expectation:15
msgid "readout mitigation method, defaults to \"constrained_least_square\""
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.expectation:17
msgid "expectation value after readout error mitigation"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.get_matrix:1
msgid "Calculate cal_matrix according to use qubit list."
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.get_matrix:3
msgid "used qubit list, defaults to None"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.get_matrix:5
msgid "cal_matrix"
msgstr ""

#: of
#: tensorcircuit.results.readout_mitigation.ReadoutMit.global_miti_readout_circ:1
msgid "Generate circuits for global calibration."
msgstr ""

#: of
#: tensorcircuit.results.readout_mitigation.ReadoutMit.global_miti_readout_circ:3
#: tensorcircuit.results.readout_mitigation.ReadoutMit.local_miti_readout_circ:3
msgid "circuit list"
msgstr ""

#: of
#: tensorcircuit.results.readout_mitigation.ReadoutMit.local_miti_readout_circ:1
msgid "Generate circuits for local calibration."
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.mapping_preprocess:1
msgid ""
"Preprocessing to deal with qubit mapping, including "
"positional_logical_mapping and logical_physical_mapping. Return "
"self.use_qubits(physical) and corresponding counts."
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.mapping_preprocess:4
msgid "raw_counts on positional_qubits"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.mapping_preprocess:12
msgid "counts on self.use_qubit(physical)"
msgstr ""

#: of
#: tensorcircuit.results.readout_mitigation.ReadoutMit.mitigate_probability:1
msgid "Get the mitigated probability."
msgstr ""

#: of
#: tensorcircuit.results.readout_mitigation.ReadoutMit.mitigate_probability:3
msgid "probability of raw count"
msgstr ""

#: of
#: tensorcircuit.results.readout_mitigation.ReadoutMit.mitigate_probability:5
msgid "mitigation methods, defaults to \"inverse\", it can also be \"square\""
msgstr ""

#: of
#: tensorcircuit.results.readout_mitigation.ReadoutMit.mitigate_probability:7
msgid "mitigated probability"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.newrange:1
msgid "Rerange the order according to used qubit list."
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.newrange:3
#: tensorcircuit.results.readout_mitigation.ReadoutMit.ubs:3
msgid "index"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.newrange:5
#: tensorcircuit.results.readout_mitigation.ReadoutMit.ubs:5
msgid "used qubit list"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.newrange:7
msgid "new index"
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.ubs:1
msgid "Help omit calibration results that not in used qubit list."
msgstr ""

#: of tensorcircuit.results.readout_mitigation.ReadoutMit.ubs:7
msgid "omitation related value"
msgstr ""

#: ../../source/api/shadows.rst:2
msgid "tensorcircuit.shadows"
msgstr ""

#: of tensorcircuit.shadows:1
msgid "Classical shadows functions"
msgstr ""

#: of tensorcircuit.shadows.entropy_shadow:1
msgid ""
"To calculate the Renyi entropy of a subsystem from shadow state or shadow"
" snapshot states"
msgstr ""

#: of tensorcircuit.shadows.entropy_shadow:3
#: tensorcircuit.shadows.expectation_ps_shadow:3
#: tensorcircuit.shadows.global_shadow_state:4
#: tensorcircuit.shadows.global_shadow_state1:5
#: tensorcircuit.shadows.global_shadow_state2:5
#: tensorcircuit.shadows.slice_sub:3
msgid "shape = (ns, repeat, nq, 2, 2) or (ns, repeat, nq)"
msgstr ""

#: of tensorcircuit.shadows.entropy_shadow:5
#: tensorcircuit.shadows.expectation_ps_shadow:5
#: tensorcircuit.shadows.global_shadow_state:6
#: tensorcircuit.shadows.global_shadow_state1:7
#: tensorcircuit.shadows.global_shadow_state2:7
msgid "shape = None or (ns, nq) or (ns, repeat, nq)"
msgstr ""

#: of tensorcircuit.shadows.entropy_shadow:7
#: tensorcircuit.shadows.global_shadow_state:8
#: tensorcircuit.shadows.global_shadow_state1:9
#: tensorcircuit.shadows.global_shadow_state2:9
#: tensorcircuit.shadows.local_snapshot_states:7
#: tensorcircuit.shadows.renyi_entropy_2:6
#: tensorcircuit.shadows.shadow_snapshots:9 tensorcircuit.shadows.slice_sub:5
msgid "qubit indices of subsystem"
msgstr ""

#: of tensorcircuit.shadows.entropy_shadow:9
msgid "order of the Renyi entropy, alpha=1 corresponds to the von Neumann entropy"
msgstr ""

#: of tensorcircuit.shadows.entropy_shadow
#: tensorcircuit.shadows.expectation_ps_shadow
#: tensorcircuit.shadows.global_shadow_state
#: tensorcircuit.shadows.global_shadow_state1
#: tensorcircuit.shadows.global_shadow_state2
#: tensorcircuit.shadows.local_snapshot_states
#: tensorcircuit.shadows.renyi_entropy_2 tensorcircuit.shadows.shadow_bound
#: tensorcircuit.shadows.shadow_snapshots tensorcircuit.shadows.slice_sub
#: tensorcircuit.zx.noise_model.Channel
msgid "type"
msgstr ""

#: of tensorcircuit.shadows.entropy_shadow:6
#: tensorcircuit.shadows.expectation_ps_shadow:6
#: tensorcircuit.shadows.global_shadow_state:7
#: tensorcircuit.shadows.global_shadow_state1:8
#: tensorcircuit.shadows.global_shadow_state2:8
#: tensorcircuit.shadows.shadow_snapshots:8
msgid "Optional[Tensor]"
msgstr ""

#: of tensorcircuit.shadows.entropy_shadow:8
#: tensorcircuit.shadows.expectation_ps_shadow:8
#: tensorcircuit.shadows.expectation_ps_shadow:10
#: tensorcircuit.shadows.expectation_ps_shadow:12
#: tensorcircuit.shadows.expectation_ps_shadow:15
#: tensorcircuit.shadows.global_shadow_state:9
#: tensorcircuit.shadows.global_shadow_state1:10
#: tensorcircuit.shadows.global_shadow_state2:10
#: tensorcircuit.shadows.local_snapshot_states:8
#: tensorcircuit.shadows.renyi_entropy_2:7
#: tensorcircuit.shadows.shadow_snapshots:10
msgid "Optional[Sequence[int]]"
msgstr ""

#: of tensorcircuit.shadows.entropy_shadow
msgid "return Renyi entropy"
msgstr ""

#: of tensorcircuit.shadows.entropy_shadow:12
#: tensorcircuit.shadows.renyi_entropy_2:9
msgid "shape = ()"
msgstr ""

#: of tensorcircuit.shadows.expectation_ps_shadow:1
msgid ""
"To calculate the expectation value of an observable on shadow snapshot "
"states"
msgstr ""

#: of tensorcircuit.shadows.expectation_ps_shadow:13
msgid ""
"or one can apply a ps structures instead of x, y, z, e.g. [1, 1, 0, 2, 3,"
" 0] for X_0X_1Y_3Z_4 defaults to None, ps can overwrite x, y and z"
msgstr ""

#: of tensorcircuit.shadows.expectation_ps_shadow:16
msgid ""
"Number of equal parts to split the shadow snapshot states to compute the "
"median of means. k=1 (default) corresponds to simply taking the mean over"
" all shadow snapshot states."
msgstr ""

#: of tensorcircuit.shadows.expectation_ps_shadow
msgid "return expectation values"
msgstr ""

#: of tensorcircuit.shadows.expectation_ps_shadow:20
msgid "shape = (k,)"
msgstr ""

#: of tensorcircuit.shadows.global_shadow_state:1
msgid ""
"To generate the global shadow state from local snapshot states or "
"snapshots and pauli strings. This is the recommended implementation as it"
" typically offers the best performance"
msgstr ""

#: of tensorcircuit.shadows.global_shadow_state
#: tensorcircuit.shadows.global_shadow_state1
#: tensorcircuit.shadows.global_shadow_state2
msgid "return gsdw_state"
msgstr ""

#: of tensorcircuit.shadows.global_shadow_state:11
#: tensorcircuit.shadows.global_shadow_state1:12
#: tensorcircuit.shadows.global_shadow_state2:12
msgid "shape = (2 ** nq, 2 ** nq)"
msgstr ""

#: of tensorcircuit.shadows.global_shadow_state1:1
msgid ""
"To generate the global snapshots states from local snapshot states or "
"snapshots and pauli strings. Note: This version uses a single pure einsum"
" which may be slower during compilation and execution for larger qubit "
"counts compared to `global_shadow_state`."
msgstr ""

#: of tensorcircuit.shadows.global_shadow_state2:1
msgid ""
"To generate the global snapshots states from local snapshot states or "
"snapshots and pauli strings. Note: This version uses vmap over einsum "
"which may be slower during compilation and execution for larger qubit "
"counts compared to `global_shadow_state`."
msgstr ""

#: of tensorcircuit.shadows.local_snapshot_states:1
msgid "To generate the local snapshots states from snapshots and pauli strings"
msgstr ""

#: of tensorcircuit.shadows.local_snapshot_states:3
#: tensorcircuit.shadows.renyi_entropy_2:4
msgid "shape = (ns, repeat, nq)"
msgstr ""

#: of tensorcircuit.shadows.local_snapshot_states:5
msgid "shape = (ns, nq) or (ns, repeat, nq)"
msgstr ""

#: of tensorcircuit.shadows.local_snapshot_states
msgid "return lss_states"
msgstr ""

#: of tensorcircuit.shadows.local_snapshot_states:10
msgid "shape = (ns, repeat, nq, 2, 2)"
msgstr ""

#: of tensorcircuit.shadows.renyi_entropy_2:1
msgid ""
"To calculate the second order Renyi entropy of a subsystem from snapshot,"
" please refer to Brydges, T. et al. Science 364, 260-263 (2019). This "
"function is not jitable."
msgstr ""

#: of tensorcircuit.shadows.renyi_entropy_2
msgid "return second order Renyi entropy"
msgstr ""

#: of tensorcircuit.shadows.shadow_bound:1
msgid ""
"Calculate the shadow bound of the Pauli observables, please refer to the "
"Theorem S1 and Lemma S3 in Huang, H.-Y., R. Kueng, and J. Preskill, 2020,"
" Nat. Phys. 16, 1050."
msgstr ""

#: of tensorcircuit.shadows.shadow_bound:4
msgid ""
"shape = (nq,) or (M, nq), where nq is the number of qubits, M is the "
"number of observables"
msgstr ""

#: of tensorcircuit.shadows.shadow_bound:6
msgid "error on the estimator"
msgstr ""

#: of tensorcircuit.shadows.shadow_bound:8
msgid "rate of failure for the bound to hold"
msgstr ""

#: of tensorcircuit.shadows.shadow_bound:5
msgid "Union[Tensor, Sequence[int]]"
msgstr ""

#: of tensorcircuit.shadows.shadow_bound:7 tensorcircuit.shadows.shadow_bound:9
msgid "float"
msgstr ""

#: of tensorcircuit.shadows.shadow_bound
msgid "return Nk"
msgstr ""

#: of tensorcircuit.shadows.shadow_bound:11
msgid "number of snapshots"
msgstr ""

#: of tensorcircuit.shadows.shadow_bound
msgid "return k"
msgstr ""

#: of tensorcircuit.shadows.shadow_bound:13
msgid ""
"number of equal parts to split the shadow snapshot states to compute the "
"median of means. k=1 (default) corresponds to simply taking the mean over"
" all shadow snapshot states."
msgstr ""

#: of tensorcircuit.shadows.shadow_snapshots:1
msgid ""
"To generate the shadow snapshots from given pauli string observables on "
"psi"
msgstr ""

#: of tensorcircuit.shadows.shadow_snapshots:3
msgid "shape = (2 ** nq,), where nq is the number of qubits"
msgstr ""

#: of tensorcircuit.shadows.shadow_snapshots:5
msgid "shape = (ns, nq), where ns is the number of pauli strings"
msgstr ""

#: of tensorcircuit.shadows.shadow_snapshots:7
msgid ""
"shape = None or (ns, repeat), where repeat is the times to measure on one"
" pauli string"
msgstr ""

#: of tensorcircuit.shadows.shadow_snapshots:11
msgid "return snapshots (True) or snapshot states (False), default=False"
msgstr ""

#: of tensorcircuit.shadows.shadow_snapshots:12
msgid "bool"
msgstr ""

#: of tensorcircuit.shadows.shadow_snapshots
msgid "return snapshots"
msgstr ""

#: of tensorcircuit.shadows.shadow_snapshots:14
msgid ""
"shape = (ns, repeat, nq) if measurement_only=True otherwise (ns, repeat, "
"nq, 2, 2)"
msgstr ""

#: of tensorcircuit.shadows.slice_sub:1
msgid "To slice off the subsystem"
msgstr ""

#: of tensorcircuit.shadows.slice_sub:6
msgid "Sequence[int]"
msgstr ""

#: of tensorcircuit.shadows.slice_sub
msgid "return subsystem"
msgstr ""

#: of tensorcircuit.shadows.slice_sub:8
msgid "shape = (ns, repeat, nq_sub, 2, 2)"
msgstr ""

#: ../../source/api/simplify.rst:2
msgid "tensorcircuit.simplify"
msgstr ""

#: of tensorcircuit.simplify:1
msgid "Tensornetwork Simplification"
msgstr ""

#: of tensorcircuit.simplify.infer_new_shape:1
msgid ""
"Get the new shape of two nodes, also supporting to return original shapes"
" of two nodes."
msgstr ""

#: of tensorcircuit.simplify.infer_new_shape:13
msgid "node one"
msgstr ""

#: of tensorcircuit.simplify.infer_new_shape:15
msgid "node two"
msgstr ""

#: of tensorcircuit.simplify.infer_new_shape:17
msgid "Whether to include original shape of two nodes, default is True."
msgstr ""

#: of tensorcircuit.simplify.infer_new_shape:19
msgid "The new shape of the two nodes."
msgstr ""

#: of tensorcircuit.simplify.pseudo_contract_between:1
msgid ""
"Contract between Node ``a`` and ``b``, with correct shape only and no "
"calculation"
msgstr ""

#: ../../source/api/stabilizercircuit.rst:2
msgid "tensorcircuit.stabilizercircuit"
msgstr ""

#: of tensorcircuit.stabilizercircuit:1
msgid "Stabilizer circuit simulator using Stim backend"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit:1
msgid ""
"Quantum circuit simulator for stabilizer circuits using Stim backend. "
"Supports Clifford operations and measurements."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.__init__:1
msgid "``StabilizerCircuit`` class based on stim package"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.__init__:5
msgid "initial state by stabilizers, defaults to None"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.__init__:7
msgid "initial state by **inverse** tableau, defaults to None"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.apply_general_gate:1
msgid "Apply a Clifford gate to the circuit."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.apply_general_gate:3
msgid "Gate to apply (must be Clifford)"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.apply_general_gate:5
#: tensorcircuit.stabilizercircuit.StabilizerCircuit.random_gate:4
#: tensorcircuit.stabilizercircuit.StabilizerCircuit.tableau_gate:4
msgid "Qubit indices to apply the gate to"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.apply_general_gate:7
msgid "Name of the gate operation, defaults to None"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.apply_general_gate:9
msgid "If non-Clifford gate is applied"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.cond_measurement:1
msgid "Measure a single qubit in the Z basis and collapse the state."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.cond_measurement:3
msgid "The index of the qubit to measure."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.cond_measurement:5
msgid "The measurement result (0 or 1)."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.cond_measure_many:1
msgid "Measure multiple qubits in the Z basis and collapse the state."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.cond_measure_many:3
msgid "The indices of the qubits to measure."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.cond_measure_many:5
msgid "A tensor containing the measurement results."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.current_circuit:1
msgid "Return the current stim circuit representation of the circuit."
msgstr ""

#: of
#: tensorcircuit.stabilizercircuit.StabilizerCircuit.current_inverse_tableau:1
msgid "Return the current inverse tableau of the circuit."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.current_simulator:1
msgid "Return the current simulator of the circuit."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.current_tableau:1
msgid "Return the current tableau of the circuit."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.depolarizing:1
msgid "Apply depolarizing noise to a qubit."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.depolarizing:3
msgid "Index of the qubit to apply noise to"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.depolarizing:5
msgid "Noise parameter (probability of depolarizing)"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.entanglement_entropy:1
msgid ""
"Calculate the entanglement entropy for a subset of qubits using "
"stabilizer formalism."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.entanglement_entropy:3
msgid "Indices of qubits to calculate entanglement entropy for"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.entanglement_entropy:5
msgid "Entanglement entropy"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.expectation_ps:1
msgid ""
"Compute exact expectation value of Pauli string using stim's direct "
"calculation."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.expectation_ps:3
msgid "Indices for Pauli X measurements"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.expectation_ps:5
msgid "Indices for Pauli Y measurements"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.expectation_ps:7
msgid "Indices for Pauli Z measurements"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.measure:1
msgid "Measure qubits in the Z basis."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.measure:3
msgid "Indices of the qubits to measure."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.measure:5
msgid ""
"If True, returns the theoretical probability of the measurement outcome. "
"defaults to False"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.measure:8
msgid ""
"A tensor containing the measurement results. If `with_prob` is True, a "
"tuple containing the results and the probability is returned."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.mid_measurement:1
msgid ""
"Perform a mid-measurement operation on a qubit on z direction. The post-"
"selection cannot be recorded in ``stim.Circuit``"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.mid_measurement:4
msgid "Index of the qubit to measure"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.mid_measurement:6
msgid "State of qubits to keep after measurement, defaults to 0 (up)"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.mid_measurement:8
msgid "Result of the mid-measurement operation"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.random_gate:1
msgid ""
"Apply a random Clifford gate to the circuit. This operation will not "
"record in qir"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.random_gate:6
#: tensorcircuit.stabilizercircuit.StabilizerCircuit.tableau_gate:8
msgid "Whether the gate is recorded in ``stim.Circuit``, defaults to False"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.sample:1
msgid "Sample measurements from the circuit."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.sample:3
msgid "Number of samples to take, defaults to None (single sample)"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.sample:5
msgid "Measurement results"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.sample_expectation_ps:1
msgid "Compute expectation value of Pauli string measurements."
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.sample_expectation_ps:3
msgid "Indices for Pauli X measurements, defaults to None"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.sample_expectation_ps:5
msgid "Indices for Pauli Y measurements, defaults to None"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.sample_expectation_ps:7
msgid "Indices for Pauli Z measurements, defaults to None"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.sample_expectation_ps:9
msgid "Number of measurement shots, defaults to None"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.state:1
msgid ""
"Return the wavefunction of the circuit. Note that the state can have "
"smaller qubit count if no gate is applied on later qubits"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.tableau_gate:1
msgid ""
"Apply a gate indicated by tableau to the circuit. This operation will not"
" record in qir"
msgstr ""

#: of tensorcircuit.stabilizercircuit.StabilizerCircuit.tableau_gate:6
msgid "stim.Tableau representation of the gate"
msgstr ""

#: ../../source/api/symbolcircuit.rst:2
msgid "tensorcircuit.symbolcircuit"
msgstr ""

#: of tensorcircuit.symbolcircuit:1
msgid "SymbolCircuit: symbolic parameterized quantum circuit."
msgstr ""

#: of tensorcircuit.symbolcircuit:3
msgid ""
"Gate parameters are sympy Symbols (or expressions). Amplitude and "
"expectation values are computed via tensor network contraction of numpy "
"object arrays, producing sympy expressions. The class also supports "
"translation to a Qiskit QuantumCircuit with Parameter objects for "
"hardware compilation reuse."
msgstr ""

#: of tensorcircuit.symbolcircuit:8
msgid ""
"Key design: inherit from Circuit, override gate registration to use "
"symbolic factories from symbolgates.py instead of the standard backend-"
"coupled ones, and override the handful of computation methods that call "
"backend.* on tensor values."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit:1
msgid "Quantum circuit with symbolic (sympy) gate parameters."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit:3
msgid ""
"Gate parameters are ``sympy.Symbol`` objects (or sympy expressions). "
"Amplitude and expectation values return sympy expressions via tensor "
"network contraction. The circuit can be translated to a Qiskit "
"``QuantumCircuit`` with ``Parameter`` objects for hardware reuse."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit:8
msgid ""
"**Backend isolation** — ``SymbolCircuit`` is permanently fixed to the "
"numpy backend regardless of any global backend setting.  Calling "
"``tc.set_backend(\"jax\")`` (or ``\"tensorflow\"``, ``\"torch\"``, etc.) "
"before or after constructing a ``SymbolCircuit`` has *no effect* on its "
"internal computation.  This is by design: the class represents all state "
"vectors and gate matrices as ``numpy.ndarray`` with ``dtype=object``, "
"whose entries are sympy expressions.  The methods ``amplitude``, "
"``wavefunction``, and ``expectation`` / ``expectation_before`` are all "
"overridden to use plain NumPy operations instead of ``tc.backend.*`` "
"calls, so they never touch the active backend."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit:19
msgid ""
"The isolation ends at :meth:`to_circuit`: the returned :class:`Circuit` "
"is a standard numerical circuit that *does* respect the global backend "
"setting at the time it is called."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit:23
#: torch.nn.modules.module.Module.apply:12
#: torch.nn.modules.module.Module.buffers:10
#: torch.nn.modules.module.Module.modules:10
#: torch.nn.modules.module.Module.named_buffers:14
#: torch.nn.modules.module.Module.named_children:5
#: torch.nn.modules.module.Module.named_modules:15
#: torch.nn.modules.module.Module.named_parameters:15
#: torch.nn.modules.module.Module.parameters:12
#: torch.nn.modules.module.Module.register_buffer:25
#: torch.nn.modules.module.Module.state_dict:38
msgid "Example::"
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.__init__:1
msgid "Initialize a SymbolCircuit with ``nqubits`` qubits."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.__init__:3
#, python-brace-format
msgid ""
"The initial state is :math:`|0\\rangle^{\\otimes n}` represented as numpy"
" object-dtype tensor network nodes (compatible with sympy)."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.__init__:6
msgid "Number of qubits."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.amplitude:1
msgid "Compute :math:`\\langle l \\vert \\psi \\rangle` symbolically."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.amplitude:3
msgid "Bitstring as a string (e.g. ``\"01\"``) or sequence of ints."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.amplitude:5
msgid "Sympy expression for the amplitude."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate:1
msgid "Override for backend isolation."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_gate_delayed:1
msgid ""
"Override for fixed gates: use symbolic gate factory instead of the "
"backend-coupled ``gatef()`` call."
msgstr ""

#: of
#: tensorcircuit.symbolcircuit.SymbolCircuit.apply_general_variable_gate_delayed:1
msgid ""
"Override for variable gates: use symbolic gate factory (sympy cos/sin) "
"instead of the backend-coupled ``gatef(**vars)`` call."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.bind:1
msgid ""
"Return a new :class:`SymbolCircuit` with some or all parameters "
"substituted (partial or full binding)."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.bind:4
msgid "Mapping from sympy Symbol to value (numeric or another sympy expression)."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.bind:7
msgid "New :class:`SymbolCircuit` with substituted parameters."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.cond_measurement:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.measure:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.measure_reference:1
#: tensorcircuit.symbolcircuit.SymbolCircuit.sample:1
msgid "Overridden to provide better error message for symbolic circuits."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.expectation:1
msgid "Compute :math:`\\langle \\psi | O | \\psi \\rangle` symbolically."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.expectation:3
msgid ""
"Pairs of ``(operator, qubit_indices)``.  The operator may be a ``Gate``, "
"a ``tn.Node``, or a plain numpy array.  Symbolic (object-dtype) operators"
" are supported."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.expectation:7
msgid "Cache the contracted state vector for repeated calls, defaults to True."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.expectation:12
#: tensorcircuit.symbolcircuit.SymbolCircuit.sample_expectation_ps:11
msgid "Sympy expression for the expectation value."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.expectation_before:1
msgid "Build the tensor network for ``<psi|O|psi>`` without contracting."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.expectation_before:3
msgid ""
"Operators may be: * A ``Gate`` / ``tn.Node`` (numerical or symbolic "
"tensor) * A plain ``np.ndarray``"
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.expectation_before:7
msgid ""
"All operator tensors are converted to numpy ``dtype=object`` for "
"compatibility with the symbolic state tensor."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.free_symbols:1
msgid "Return the set of all free sympy Symbols used as gate parameters."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.free_symbols:3
msgid "Set of sympy Symbols."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.get_quoperator:1
msgid "Get symbolic QuOperator representation."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.matrix:1
msgid "Compute the symbolic unitary matrix of the circuit."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.probability:1
msgid "Compute the symbolic probability distribution."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.projected_subsystem:1
msgid "Compute symbolic projected subsystem."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.sample_expectation_ps:1
msgid "Symbolic execution for analytical Pauli string expectation."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.sample_expectation_ps:3
msgid "List of indices for Pauli X."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.sample_expectation_ps:5
msgid "List of indices for Pauli Y."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.sample_expectation_ps:7
msgid "List of indices for Pauli Z."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.sample_expectation_ps:9
msgid "Must be None for analytical symbolic result."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.wavefunction:1
msgid "Compute the symbolic output state vector."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.wavefunction:3
msgid ""
"Returns a numpy object array containing sympy expressions. Only practical"
" for small qubit counts where the full vector is manageable."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.wavefunction:6
msgid ""
"Shape of output: ``\"default\"`` → 1-D, ``\"ket\"`` → column, ``\"bra\"``"
" → row.  Defaults to ``\"default\"``."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.wavefunction:9
msgid "Numpy object array of sympy expressions."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.to_circuit:1
msgid "Convert to a numerical :class:`Circuit` by binding symbolic parameters."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.to_circuit:3
#, python-brace-format
msgid ""
"Mapping from sympy Symbol to numerical value. Pass ``None`` (or ``{}``) "
"only if the circuit has no free symbols."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.to_circuit:6
msgid "A fully numerical :class:`Circuit`."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.to_qiskit:1
msgid "Translate to a Qiskit ``QuantumCircuit`` with ``Parameter`` objects."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.to_qiskit:3
msgid ""
"Each ``sympy.Symbol`` used as a gate parameter is mapped to a Qiskit "
"``Parameter`` with the same name.  The resulting circuit can be bound "
"with :meth:`qiskit.QuantumCircuit.assign_parameters` and executed on "
"hardware or simulators."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.to_qiskit:8
msgid ""
"Simple arithmetic expressions involving symbols are translated when "
"possible (``+``, ``-``, ``*``, ``/``).  Complex expressions (``sin``, "
"``cos``, etc.) that appear directly as *gate-level parameters* will raise"
" a ``NotImplementedError`` — but note that rotation angles are passed at "
"the gate level (e.g. ``rx(theta)``), not as matrix entries, so this is "
"rarely an issue for standard circuit translation."
msgstr ""

#: of tensorcircuit.symbolcircuit.SymbolCircuit.to_qiskit:15
msgid "Qiskit ``QuantumCircuit`` with symbolic Parameters."
msgstr ""

#: ../../source/api/symbolgates.rst:2
msgid "tensorcircuit.symbolgates"
msgstr ""

#: of tensorcircuit.symbolgates:1
msgid "Symbolic gate matrix factories using sympy."
msgstr ""

#: of tensorcircuit.symbolgates:3
msgid ""
"These return Gate objects containing numpy object arrays with sympy "
"expressions, compatible with tn.Node and the tc.contractor contraction "
"pipeline."
msgstr ""

#: of tensorcircuit.symbolgates:6
msgid ""
"Convention: for an n-qubit gate, the tensor is shaped [2]*2n with the "
"first n axes being output legs and the last n axes being input legs, "
"matching the Gate convention in tensorcircuit."
msgstr ""

#: of tensorcircuit.symbolgates.sym_any:1
msgid ""
"Symbolic 'any' gate from a unitary matrix. Extra keyword arguments are "
"ignored (compatibility with delayed apply)."
msgstr ""

#: ../../source/api/templates.rst:2
msgid "tensorcircuit.templates"
msgstr ""

#: ../../source/api/templates/ansatz.rst:2
msgid "tensorcircuit.templates.ansatz"
msgstr ""

#: of tensorcircuit.templates.ansatz:1
msgid "Shortcuts for reusable circuit ansatz"
msgstr ""

#: of tensorcircuit.templates.ansatz.QAOA_ansatz_for_Ising:1
msgid ""
"Construct the QAOA ansatz for the Ising Model. The number of qubits is "
"determined by `pauli_terms`."
msgstr ""

#: of tensorcircuit.templates.ansatz.QAOA_ansatz_for_Ising
msgid "pauli_terms"
msgstr ""

#: of tensorcircuit.templates.ansatz.QAOA_ansatz_for_Ising:10
msgid "QAOA ansatz for Ising model."
msgstr ""

#: ../../source/api/templates/blocks.rst:2
msgid "tensorcircuit.templates.blocks"
msgstr ""

#: of tensorcircuit.templates.blocks:1 tensorcircuit.templates.measurements:1
msgid "Shortcuts for measurement patterns on circuit"
msgstr ""

#: of tensorcircuit.templates.blocks.Bell_pair_block:1
msgid ""
"For each pair in links, the input product state |00> is transformed as "
"(01>-|10>)"
msgstr ""

#: of tensorcircuit.templates.blocks.Bell_pair_block:3
#: tensorcircuit.templates.blocks.qft:3
msgid "Circuit in"
msgstr ""

#: of tensorcircuit.templates.blocks.Bell_pair_block:5
msgid ""
"pairs indices for Bell pairs, defaults to None, corresponds to neighbor "
"links"
msgstr ""

#: of tensorcircuit.templates.blocks.Bell_pair_block:7
msgid "Circuit out"
msgstr ""

#: of tensorcircuit.templates.blocks.example_block:1
msgid ""
"The circuit ansatz is firstly one layer of Hadamard gates and then we "
"have ``nlayers`` blocks of :math:`e^{i\\theta Z_iZ_{i+1}}` two-qubit gate"
" in ladder layout, following rx gate."
msgstr ""

#: of tensorcircuit.templates.blocks.example_block:5
msgid "The circuit"
msgstr ""

#: of tensorcircuit.templates.blocks.example_block:7
msgid "paramter tensor with 2*nlayer*n elements"
msgstr ""

#: of tensorcircuit.templates.blocks.example_block:9
msgid "number of ZZ+RX blocks, defaults to 2"
msgstr ""

#: of tensorcircuit.templates.blocks.example_block:11
msgid "whether use SVD split to reduce ZZ gate bond dimension, defaults to False"
msgstr ""

#: of tensorcircuit.templates.blocks.example_block:14
msgid "The circuit with example ansatz attached"
msgstr ""

#: of tensorcircuit.templates.blocks.qft:1
msgid ""
"This function applies quantum fourier transformation (QFT) to the "
"selected circuit lines"
msgstr ""

#: of tensorcircuit.templates.blocks.qft:5
msgid "the indices of the circuit lines to apply QFT"
msgstr ""

#: of tensorcircuit.templates.blocks.qft:7
msgid "Whether to include the final swaps in the QFT"
msgstr ""

#: of tensorcircuit.templates.blocks.qft:9
msgid "If True, the inverse Fourier transform is constructed"
msgstr ""

#: of tensorcircuit.templates.blocks.qft:11
msgid "If True, barriers are inserted as visualization improvement"
msgstr ""

#: of tensorcircuit.templates.blocks.qft:13
msgid "Circuit c"
msgstr ""

#: of tensorcircuit.templates.blocks.state_centric:1
msgid ""
"Function decorator wraps the function with the first input and output in "
"the format of circuit, the wrapped function has the first input and the "
"output as the state tensor."
msgstr ""

#: of tensorcircuit.templates.blocks.state_centric:4
msgid "Function with the fist input and the output as ``Circuit`` object."
msgstr ""

#: of tensorcircuit.templates.blocks.state_centric:6
msgid ""
"Wrapped function with the first input and the output as the state tensor "
"correspondingly."
msgstr ""

#: ../../source/api/templates/chems.rst:2
msgid "tensorcircuit.templates.chems"
msgstr ""

#: of tensorcircuit.templates.chems:1
msgid "Useful utilities for quantum chemistry related task"
msgstr ""

#: ../../source/api/templates/conversions.rst:2
msgid "tensorcircuit.templates.conversions"
msgstr ""

#: of tensorcircuit.templates.conversions:1
msgid "helper functions for conversions"
msgstr ""

#: of tensorcircuit.templates.conversions.QUBO_to_Ising:1
msgid ""
"Cnvert the Q matrix into a the indication of pauli terms, the "
"corresponding weights, and the offset. The outputs are used to construct "
"an Ising Hamiltonian for QAOA."
msgstr ""

#: of tensorcircuit.templates.conversions.QUBO_to_Ising:4
msgid "The n-by-n square and symmetric Q-matrix."
msgstr ""

#: of tensorcircuit.templates.conversions.QUBO_to_Ising
msgid "return pauli_terms"
msgstr ""

#: of tensorcircuit.templates.conversions.QUBO_to_Ising:5
msgid "A list of 0/1 series, where each element represents a Pauli term."
msgstr ""

#: of tensorcircuit.templates.conversions.QUBO_to_Ising:6
msgid ""
"A value of 1 indicates the presence of a Pauli-Z operator, while a value "
"of 0 indicates its absence. :return weights: A list of weights "
"corresponding to each Pauli term. :return offset: A float representing "
"the offset term of the Ising Hamiltonian."
msgstr ""

#: of tensorcircuit.templates.conversions.get_ps:1
msgid ""
"Get Pauli string array and weights array for a qubit Hamiltonian as a sum"
" of Pauli strings defined in openfermion ``QubitOperator``."
msgstr ""

#: of tensorcircuit.templates.conversions.get_ps:4
msgid "``openfermion.ops.operators.qubit_operator.QubitOperator``"
msgstr ""

#: of tensorcircuit.templates.conversions.get_ps:6
msgid "The number of qubits"
msgstr ""

#: of tensorcircuit.templates.conversions.get_ps:8
msgid "Pauli String array and weights array"
msgstr ""

#: ../../source/api/templates/dataset.rst:2
msgid "tensorcircuit.templates.dataset"
msgstr ""

#: of tensorcircuit.templates.dataset:1
msgid "Quantum machine learning related data preprocessing and embedding"
msgstr ""

#: ../../source/api/templates/graphs.rst:2
msgid "tensorcircuit.templates.graphs"
msgstr ""

#: of tensorcircuit.templates.graphs:1
msgid "Some common graphs and lattices"
msgstr ""

#: of tensorcircuit.templates.graphs.Grid2DCoord:1
msgid "Two-dimensional grid lattice"
msgstr ""

#: of tensorcircuit.templates.graphs.Grid2DCoord.__init__:1
msgid "number of rows"
msgstr ""

#: of tensorcircuit.templates.graphs.Grid2DCoord.__init__:3
msgid "number of cols"
msgstr ""

#: of tensorcircuit.templates.graphs.Grid2DCoord.all_cols:1
msgid "return all col edge with 1d index encoding"
msgstr ""

#: of tensorcircuit.templates.graphs.Grid2DCoord.all_cols:3
#: tensorcircuit.templates.graphs.Grid2DCoord.all_rows:3
msgid ""
"whether to include pbc edges (periodic boundary condition), defaults to "
"False"
msgstr ""

#: of tensorcircuit.templates.graphs.Grid2DCoord.all_cols:6
msgid "list of col edge"
msgstr ""

#: of tensorcircuit.templates.graphs.Grid2DCoord.all_rows:1
msgid "return all row edge with 1d index encoding"
msgstr ""

#: of tensorcircuit.templates.graphs.Grid2DCoord.all_rows:6
msgid "list of row edge"
msgstr ""

#: of tensorcircuit.templates.graphs.Grid2DCoord.lattice_graph:1
msgid "Get the 2D grid lattice in ``nx.Graph`` format"
msgstr ""

#: of tensorcircuit.templates.graphs.Grid2DCoord.lattice_graph:3
msgid ""
"whether to include pbc edges (periodic boundary condition), defaults to "
"True"
msgstr ""

#: of tensorcircuit.templates.graphs.Line1D:1
msgid "1D chain with ``n`` sites"
msgstr ""

#: of tensorcircuit.templates.graphs.Line1D:5
#: tensorcircuit.templates.measurements.heisenberg_measurements:34
msgid "[description], defaults to True"
msgstr ""

#: ../../source/api/templates/hamiltonians.rst:2
msgid "tensorcircuit.templates.hamiltonians"
msgstr ""

#: of tensorcircuit.templates.hamiltonians.heisenberg_hamiltonian:1
msgid ""
"Generates the sparse matrix of the Heisenberg Hamiltonian for a given "
"lattice."
msgstr ""

#: of tensorcircuit.templates.hamiltonians.heisenberg_hamiltonian:3
#, python-brace-format
msgid ""
"The Heisenberg Hamiltonian is defined as: :math:`H = J\\sum_{i,j} (X_i "
"X_j + Y_i Y_j + Z_i Z_j)` where the sum is over a specified set of "
"interacting pairs {i,j}."
msgstr ""

#: of tensorcircuit.templates.hamiltonians.heisenberg_hamiltonian:7
msgid ""
"An instance of a class derived from AbstractLattice, which provides the "
"geometric information of the system."
msgstr ""

#: of tensorcircuit.templates.hamiltonians.heisenberg_hamiltonian:10
msgid ""
"The coupling constants. Can be a single float for an isotropic model "
"(Jx=Jy=Jz) or a list/tuple of 3 floats for an anisotropic model (Jx, Jy, "
"Jz). Defaults to 1.0."
msgstr ""

#: of tensorcircuit.templates.hamiltonians.heisenberg_hamiltonian:14
msgid ""
"Defines the range of interactions. - \"neighbors\": Includes only "
"nearest-neighbor pairs (default). - \"all\": Includes all unique pairs of"
" sites."
msgstr ""

#: of tensorcircuit.templates.hamiltonians.heisenberg_hamiltonian:18
#: tensorcircuit.templates.hamiltonians.rydberg_hamiltonian:28
msgid "The Hamiltonian as a backend-agnostic sparse matrix."
msgstr ""

#: of tensorcircuit.templates.hamiltonians.rydberg_hamiltonian:1
msgid "Generates the sparse matrix of the Rydberg atom array Hamiltonian."
msgstr ""

#: of tensorcircuit.templates.hamiltonians.rydberg_hamiltonian:3
msgid "The Hamiltonian is defined as: .. math::"
msgstr ""

#: of tensorcircuit.templates.hamiltonians.rydberg_hamiltonian:6
#, python-brace-format
msgid "H = \\sum_i \\frac{\\Omega}{2} X_i"
msgstr ""

#: of tensorcircuit.templates.hamiltonians.rydberg_hamiltonian:7
#, python-brace-format
msgid "\\sum_i \\frac{\\delta}{2} \\bigl(1 - Z_i \\bigr)"
msgstr ""

#: of tensorcircuit.templates.hamiltonians.rydberg_hamiltonian:8
msgid "\\sum_{i<j} \\frac{V_{ij}}{4} \\bigl(1 - Z_i \\bigr)\\bigl(1 - Z_j \\bigr)"
msgstr ""

#: of tensorcircuit.templates.hamiltonians.rydberg_hamiltonian:10
#, python-brace-format
msgid "= \\sum_i \\frac{\\Omega}{2} X_i"
msgstr ""

#: of tensorcircuit.templates.hamiltonians.rydberg_hamiltonian:11
#, python-brace-format
msgid "\\sum_i \\frac{\\delta}{2} Z_i"
msgstr ""

#: of tensorcircuit.templates.hamiltonians.rydberg_hamiltonian:12
msgid "\\sum_{i<j} \\frac{V_{ij}}{4}\\,\\bigl(Z_i Z_j - Z_i - Z_j \\bigr)"
msgstr ""

#: of tensorcircuit.templates.hamiltonians.rydberg_hamiltonian:14
#, python-brace-format
msgid "where :math:`V_{ij} = C6 / |r_i - r_j|^6`."
msgstr ""

#: of tensorcircuit.templates.hamiltonians.rydberg_hamiltonian:16
msgid ""
"Note: Constant energy offset terms (proportional to the identity "
"operator) are ignored in this implementation."
msgstr ""

#: of tensorcircuit.templates.hamiltonians.rydberg_hamiltonian:19
msgid ""
"An instance of a class derived from AbstractLattice, which provides site "
"coordinates and the distance matrix."
msgstr ""

#: of tensorcircuit.templates.hamiltonians.rydberg_hamiltonian:22
msgid "The Rabi frequency (Ω) of the driving laser field."
msgstr ""

#: of tensorcircuit.templates.hamiltonians.rydberg_hamiltonian:24
msgid "The laser detuning (δ)."
msgstr ""

#: of tensorcircuit.templates.hamiltonians.rydberg_hamiltonian:26
msgid "The Van der Waals interaction coefficient (C6)."
msgstr ""

#: ../../source/api/templates/lattice.rst:2
msgid "tensorcircuit.templates.lattice"
msgstr ""

#: of tensorcircuit.templates.lattice:1
msgid "The lattice module for defining and manipulating lattice geometries."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice:1
msgid "Abstract base class for describing lattice systems."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice:3
msgid ""
"This class defines the common interface for all lattice structures, "
"providing access to fundamental properties like site information (count, "
"coordinates, identifiers) and neighbor relationships. Subclasses are "
"responsible for implementing the specific logic for generating the "
"lattice points and calculating neighbor connections."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice:9
msgid "The spatial dimension of the lattice (e.g., 1, 2, 3)."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.__init__:1
msgid "Initializes the base lattice class."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.dimensionality:1
#: tensorcircuit.templates.lattice.ChainLattice.dimensionality:1
#: tensorcircuit.templates.lattice.CheckerboardLattice.dimensionality:1
#: tensorcircuit.templates.lattice.CubicLattice.dimensionality:1
#: tensorcircuit.templates.lattice.CustomizeLattice.dimensionality:1
#: tensorcircuit.templates.lattice.DimerizedChainLattice.dimensionality:1
#: tensorcircuit.templates.lattice.HoneycombLattice.dimensionality:1
#: tensorcircuit.templates.lattice.KagomeLattice.dimensionality:1
#: tensorcircuit.templates.lattice.LiebLattice.dimensionality:1
#: tensorcircuit.templates.lattice.RectangularLattice.dimensionality:1
#: tensorcircuit.templates.lattice.SquareLattice.dimensionality:1
#: tensorcircuit.templates.lattice.TILattice.dimensionality:1
#: tensorcircuit.templates.lattice.TriangularLattice.dimensionality:1
msgid "Returns the spatial dimension of the lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.distance_matrix:1
#: tensorcircuit.templates.lattice.ChainLattice.distance_matrix:1
#: tensorcircuit.templates.lattice.CheckerboardLattice.distance_matrix:1
#: tensorcircuit.templates.lattice.CubicLattice.distance_matrix:1
#: tensorcircuit.templates.lattice.CustomizeLattice.distance_matrix:1
#: tensorcircuit.templates.lattice.DimerizedChainLattice.distance_matrix:1
#: tensorcircuit.templates.lattice.HoneycombLattice.distance_matrix:1
#: tensorcircuit.templates.lattice.KagomeLattice.distance_matrix:1
#: tensorcircuit.templates.lattice.LiebLattice.distance_matrix:1
#: tensorcircuit.templates.lattice.RectangularLattice.distance_matrix:1
#: tensorcircuit.templates.lattice.SquareLattice.distance_matrix:1
#: tensorcircuit.templates.lattice.TILattice.distance_matrix:1
#: tensorcircuit.templates.lattice.TriangularLattice.distance_matrix:1
msgid ""
"Returns the full N x N distance matrix. The matrix is computed on the "
"first access and then cached for subsequent calls. This computation can "
"be expensive for large lattices."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_all_pairs:1
msgid "Returns a list of all unique pairs of site indices (i, j) where i < j."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_all_pairs:3
msgid ""
"This method provides all-to-all connectivity, useful for Hamiltonians "
"where every site interacts with every other site."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_all_pairs:6
msgid ""
"Note on Differentiability: This method provides a static list of index "
"pairs and is not differentiable itself. However, it is designed to be "
"used in combination with the fully differentiable ``distance_matrix`` "
"property. By using the pairs from this method to index into the "
"``distance_matrix``, one can construct differentiable objective functions"
" based on all-pair interactions, effectively bypassing the non-"
"differentiable ``get_neighbor_pairs`` method for geometry optimization "
"tasks."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_all_pairs:14
msgid "A list of tuples, where each tuple is a unique pair of site indices."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_coordinates:1
msgid "Gets the spatial coordinates of a site by its integer index."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_coordinates:3
#: tensorcircuit.templates.lattice.AbstractLattice.get_identifier:3
msgid "The integer index of the site."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_coordinates:5
#: tensorcircuit.templates.lattice.AbstractLattice.get_identifier:5
msgid "If the site index is out of range."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_coordinates:6
msgid "The spatial coordinates as a NumPy array."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_identifier:1
msgid "Gets the abstract identifier of a site by its integer index."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_identifier:6
msgid "The unique, hashable identifier of the site."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_index:1
msgid "Gets the integer index of a site by its unique identifier."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_index:3
msgid "The unique identifier of the site."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_index:5
msgid "If the identifier is not found in the lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_index:6
msgid "The corresponding integer index of the site."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_neighbor_pairs:1
msgid "Gets all pairs of k-th nearest neighbors, representing bonds."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_neighbor_pairs:3
msgid "The order of the neighbors to consider. Defaults to 1."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_neighbor_pairs:6
msgid ""
"If True, returns only one representation for each pair (i, j) such that i"
" < j, avoiding duplicates like (j, i). If False, returns all directed "
"pairs. Defaults to True."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_neighbor_pairs:11
msgid "A list of tuples, where each tuple is a pair of neighbor indices."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_neighbors:1
msgid "Gets the list of k-th nearest neighbor indices for a given site."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_neighbors:3
msgid "The integer index of the center site."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_neighbors:5
msgid ""
"The order of the neighbors, where k=1 corresponds to nearest neighbors "
"(NN), k=2 to next-nearest neighbors (NNN), and so on. Defaults to 1."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_neighbors:9
msgid ""
"A list of integer indices for the neighboring sites. Returns an empty "
"list if neighbors for the given `k` have not been pre-calculated or if "
"the site has no such neighbors."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_site_info:1
msgid "Gets all information for a single site."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_site_info:3
msgid ""
"This method provides a convenient way to retrieve all relevant data for a"
" site (its index, identifier, and coordinates) by using either its "
"integer index or its unique identifier."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_site_info:7
msgid "The integer index or the unique identifier of the site to look up."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_site_info:10
msgid "If the given index is out of bounds."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_site_info:11
msgid "If the given identifier is not found in the lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.get_site_info:12
msgid ""
"A tuple containing: - The site's integer index. - The site's unique "
"identifier. - The site's coordinates as a NumPy array."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.num_sites:1
#: tensorcircuit.templates.lattice.ChainLattice.num_sites:1
#: tensorcircuit.templates.lattice.CheckerboardLattice.num_sites:1
#: tensorcircuit.templates.lattice.CubicLattice.num_sites:1
#: tensorcircuit.templates.lattice.CustomizeLattice.num_sites:1
#: tensorcircuit.templates.lattice.DimerizedChainLattice.num_sites:1
#: tensorcircuit.templates.lattice.HoneycombLattice.num_sites:1
#: tensorcircuit.templates.lattice.KagomeLattice.num_sites:1
#: tensorcircuit.templates.lattice.LiebLattice.num_sites:1
#: tensorcircuit.templates.lattice.RectangularLattice.num_sites:1
#: tensorcircuit.templates.lattice.SquareLattice.num_sites:1
#: tensorcircuit.templates.lattice.TILattice.num_sites:1
#: tensorcircuit.templates.lattice.TriangularLattice.num_sites:1
msgid "Returns the total number of sites (N) in the lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.show:1
msgid "Visualizes the lattice structure using Matplotlib."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.show:3
msgid ""
"This method supports 1D, 2D, and 3D plotting. For 1D lattices, sites are "
"plotted along the x-axis."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.show:6
msgid "If True, displays the integer index next to each site. Defaults to False."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.show:9
msgid ""
"If True, displays the unique identifier next to each site. Defaults to "
"False."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.show:12
msgid ""
"Specifies which order of neighbor bonds to draw (e.g., 1 for NN, 2 for "
"NNN). If None, no bonds are drawn. If the specified neighbors have not "
"been calculated, a warning is printed. Defaults to None."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.show:17
msgid ""
"An existing Matplotlib Axes object to plot on. If None, a new Figure and "
"Axes are created automatically. Defaults to None."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.show:20
msgid ""
"A dictionary of keyword arguments for customizing bond appearance, passed"
" directly to the Matplotlib plot function. Defaults to None."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.show:24
msgid ""
"Additional keyword arguments to be passed directly to the "
"`matplotlib.pyplot.scatter` function for customizing site appearance."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.sites:1
msgid "Returns an iterator over all sites in the lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.sites:3
msgid ""
"This provides a convenient way to loop through all sites, for example: "
"`for idx, ident, coords in my_lattice.sites(): ...`"
msgstr ""

#: of tensorcircuit.templates.lattice.AbstractLattice.sites:6
msgid ""
"An iterator where each item is a tuple containing the site's index, "
"identifier, and coordinates."
msgstr ""

#: of tensorcircuit.templates.lattice.ChainLattice:1
msgid "A 1D chain (simple Bravais lattice)."
msgstr ""

#: of tensorcircuit.templates.lattice.ChainLattice:3
msgid "A tuple `(N,)` specifying the number of sites in the chain."
msgstr ""

#: of tensorcircuit.templates.lattice.ChainLattice:5
#: tensorcircuit.templates.lattice.SquareLattice:10
msgid "The distance between two adjacent sites. Defaults to 1.0."
msgstr ""

#: of tensorcircuit.templates.lattice.ChainLattice:7
#: tensorcircuit.templates.lattice.DimerizedChainLattice:11
msgid "Specifies if periodic boundary conditions are applied. Defaults to True."
msgstr ""

#: of tensorcircuit.templates.lattice.ChainLattice:9
#: tensorcircuit.templates.lattice.CheckerboardLattice:11
#: tensorcircuit.templates.lattice.CubicLattice:11
#: tensorcircuit.templates.lattice.CustomizeLattice:19
#: tensorcircuit.templates.lattice.DimerizedChainLattice:13
#: tensorcircuit.templates.lattice.HoneycombLattice:16
#: tensorcircuit.templates.lattice.KagomeLattice:11
#: tensorcircuit.templates.lattice.LiebLattice:12
#: tensorcircuit.templates.lattice.RectangularLattice:13
#: tensorcircuit.templates.lattice.SquareLattice:17
#: tensorcircuit.templates.lattice.TILattice:31
#: tensorcircuit.templates.lattice.TriangularLattice:14
msgid ""
"If specified, pre-computes neighbor relationships up to the given order "
"`k` upon initialization. Defaults to None."
msgstr ""

#: of tensorcircuit.templates.lattice.ChainLattice.__init__:1
#: tensorcircuit.templates.lattice.CheckerboardLattice.__init__:1
#: tensorcircuit.templates.lattice.CubicLattice.__init__:1
#: tensorcircuit.templates.lattice.DimerizedChainLattice.__init__:1
#: tensorcircuit.templates.lattice.KagomeLattice.__init__:1
#: tensorcircuit.templates.lattice.RectangularLattice.__init__:1
#: tensorcircuit.templates.lattice.TILattice.__init__:1
msgid "Initializes the Translationally Invariant Lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.CheckerboardLattice:1
msgid "A 2D checkerboard lattice (a square lattice with an AB sublattice)."
msgstr ""

#: of tensorcircuit.templates.lattice.CheckerboardLattice:3
msgid "The unit cell is a square rotated by 45 degrees, containing two sites."
msgstr ""

#: of tensorcircuit.templates.lattice.CheckerboardLattice:5
msgid ""
"A tuple (Nx, Ny) specifying the number of unit cells. Total sites will be"
" 2*Nx*Ny."
msgstr ""

#: of tensorcircuit.templates.lattice.CheckerboardLattice:7
msgid "The bond length between nearest neighbors. Defaults to 1.0."
msgstr ""

#: of tensorcircuit.templates.lattice.CheckerboardLattice:9
#: tensorcircuit.templates.lattice.CubicLattice:9
#: tensorcircuit.templates.lattice.HoneycombLattice:13
#: tensorcircuit.templates.lattice.KagomeLattice:9
#: tensorcircuit.templates.lattice.LiebLattice:10
#: tensorcircuit.templates.lattice.RectangularLattice:11
#: tensorcircuit.templates.lattice.TriangularLattice:11
msgid "Specifies periodic boundary conditions. Defaults to True."
msgstr ""

#: of tensorcircuit.templates.lattice.CubicLattice:1
msgid "A 3D cubic lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.CubicLattice:3
msgid "This is a simple Bravais lattice, the 3D generalization of SquareLattice."
msgstr ""

#: of tensorcircuit.templates.lattice.CubicLattice:5
msgid "A tuple (Nx, Ny, Nz) specifying the number of sites."
msgstr ""

#: of tensorcircuit.templates.lattice.CubicLattice:7
msgid "The distance between adjacent sites. Defaults to 1.0."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice:1
msgid "A general lattice built from an explicit list of sites and coordinates."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice:3
msgid ""
"This class is suitable for creating lattices with arbitrary geometries, "
"such as finite clusters, disordered systems, or any custom structure that"
" does not have translational symmetry. The lattice is defined simply by "
"providing lists of identifiers and coordinates for each site."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice:8
#: tensorcircuit.templates.lattice.TILattice:13
msgid "The spatial dimension of the lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice:10
msgid ""
"A list of unique, hashable identifiers for the sites. The length must "
"match `coordinates`."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice:13
msgid ""
"A list of site coordinates. Each coordinate should be a list of floats or"
" a NumPy array."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice:17
msgid ""
"If the lengths of `identifiers` and `coordinates` lists do not match, or "
"if a coordinate's dimension is incorrect."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice.__init__:1
msgid "Initializes the CustomizeLattice."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice.add_sites:1
msgid "Adds new sites to the lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice.add_sites:3
msgid ""
"This operation modifies the lattice in-place. After adding sites, any "
"previously computed neighbor information is cleared and must be "
"recalculated."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice.add_sites:7
msgid "A list of unique identifiers for the new sites."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice.add_sites:9
msgid ""
"The coordinates for the new sites. Can be a list of lists, a NumPy array,"
" or a backend-compatible tensor (e.g., jax.numpy.ndarray)."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice.from_lattice:1
msgid "Creates a CustomizeLattice instance from any existing lattice object."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice.from_lattice:3
msgid ""
"This is useful for 'detaching' a procedurally generated lattice (like a "
"SquareLattice) into a customizable one for further modifications, such as"
" adding defects or extra sites."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice.from_lattice:7
msgid "An instance of any AbstractLattice subclass."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice.from_lattice:9
msgid "A new CustomizeLattice instance with the same sites."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice.remove_sites:1
msgid "Removes specified sites from the lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice.remove_sites:3
msgid ""
"This operation modifies the lattice in-place. After removing sites, all "
"site indices are re-calculated, and any previously computed neighbor "
"information is cleared."
msgstr ""

#: of tensorcircuit.templates.lattice.CustomizeLattice.remove_sites:7
msgid "A list of identifiers for the sites to be removed."
msgstr ""

#: of tensorcircuit.templates.lattice.DimerizedChainLattice:1
msgid "A 1D chain with an AB sublattice (dimerized chain)."
msgstr ""

#: of tensorcircuit.templates.lattice.DimerizedChainLattice:3
msgid "The unit cell contains two sites, A and B. The bond length is uniform."
msgstr ""

#: of tensorcircuit.templates.lattice.DimerizedChainLattice:5
msgid ""
"A tuple `(N,)` specifying the number of **unit cells**. The total number "
"of sites in the chain will be `2 * N`, as each unit cell contains two "
"sites."
msgstr ""

#: of tensorcircuit.templates.lattice.DimerizedChainLattice:9
msgid "The distance between two adjacent sites (bond length). Defaults to 1.0."
msgstr ""

#: of tensorcircuit.templates.lattice.HoneycombLattice:1
msgid "A 2D honeycomb lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.HoneycombLattice:3
msgid ""
"This is a classic example of a composite lattice. It consists of a two-"
"site basis (sublattices A and B) on an underlying triangular Bravais "
"lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.HoneycombLattice:7
#: tensorcircuit.templates.lattice.TriangularLattice:5
msgid ""
"A tuple (Nx, Ny) specifying the number of unit cells along the two "
"lattice vector directions."
msgstr ""

#: of tensorcircuit.templates.lattice.HoneycombLattice:10
#: tensorcircuit.templates.lattice.TriangularLattice:8
msgid ""
"The bond length, i.e., the distance between two nearest neighbor sites. "
"Defaults to 1.0."
msgstr ""

#: of tensorcircuit.templates.lattice.HoneycombLattice.__init__:1
msgid "Initializes the HoneycombLattice."
msgstr ""

#: of tensorcircuit.templates.lattice.KagomeLattice:1
msgid "A 2D Kagome lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.KagomeLattice:3
msgid "This is a lattice with a three-site basis on a triangular Bravais lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.KagomeLattice:5
#: tensorcircuit.templates.lattice.LiebLattice:6
msgid ""
"A tuple (Nx, Ny) specifying the number of unit cells. Total sites will be"
" 3*Nx*Ny."
msgstr ""

#: of tensorcircuit.templates.lattice.KagomeLattice:7
#: tensorcircuit.templates.lattice.LiebLattice:8
msgid "The bond length. Defaults to 1.0."
msgstr ""

#: of tensorcircuit.templates.lattice.LiebLattice:1
msgid "A 2D Lieb lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.LiebLattice:3
msgid ""
"This is a lattice with a three-site basis on a square Bravais lattice. It"
" has sites at the corners and centers of the edges of a square."
msgstr ""

#: of tensorcircuit.templates.lattice.LiebLattice.__init__:1
msgid "Initializes the LiebLattice."
msgstr ""

#: of tensorcircuit.templates.lattice.RectangularLattice:1
msgid "A 2D rectangular lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.RectangularLattice:3
msgid ""
"This is a generalization of the SquareLattice where the lattice constants"
" in the x and y directions can be different."
msgstr ""

#: of tensorcircuit.templates.lattice.RectangularLattice:6
msgid "A tuple (Nx, Ny) specifying the number of sites in x and y."
msgstr ""

#: of tensorcircuit.templates.lattice.RectangularLattice:8
msgid ""
"The distance between adjacent sites in the x and y directions, e.g., (ax,"
" ay). Defaults to (1.0, 1.0)."
msgstr ""

#: of tensorcircuit.templates.lattice.SquareLattice:1
msgid "A 2D square lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.SquareLattice:3
msgid ""
"This is a concrete implementation of a translationally invariant lattice "
"representing a simple square grid. It is a Bravais lattice with a single-"
"site basis."
msgstr ""

#: of tensorcircuit.templates.lattice.SquareLattice:7
msgid ""
"A tuple (Nx, Ny) specifying the number of unit cells (sites) in the x and"
" y directions."
msgstr ""

#: of tensorcircuit.templates.lattice.SquareLattice:13
msgid ""
"Specifies periodic boundary conditions. Can be a single boolean for all "
"dimensions or a tuple of booleans for each dimension individually. "
"Defaults to True."
msgstr ""

#: of tensorcircuit.templates.lattice.SquareLattice.__init__:1
msgid "Initializes the SquareLattice."
msgstr ""

#: of tensorcircuit.templates.lattice.TILattice:1
msgid "Describes a periodic lattice with translational invariance."
msgstr ""

#: of tensorcircuit.templates.lattice.TILattice:3
msgid ""
"This class serves as a base for any lattice defined by a repeating unit "
"cell. The geometry is specified by lattice vectors, the coordinates of "
"basis sites within a unit cell, and the total size of the lattice in "
"terms of unit cells."
msgstr ""

#: of tensorcircuit.templates.lattice.TILattice:8
msgid ""
"The site identifier for this class is a tuple in the format of "
"`(uc_coord_1, ..., uc_coord_d, basis_index)`, where `uc_coord` represents"
" the integer coordinate of the unit cell and `basis_index` is the index "
"of the site within that unit cell's basis."
msgstr ""

#: of tensorcircuit.templates.lattice.TILattice:15
msgid ""
"The lattice vectors defining the unit cell, given as row vectors. Shape: "
"(dimensionality, dimensionality). For example, in 2D: `np.array([[ax, "
"ay], [bx, by]])`."
msgstr ""

#: of tensorcircuit.templates.lattice.TILattice:19
msgid ""
"The Cartesian coordinates of the basis sites within the unit cell. Shape:"
" (num_basis_sites, dimensionality). For a simple Bravais lattice, this "
"would be `np.array([[0, 0]])`."
msgstr ""

#: of tensorcircuit.templates.lattice.TILattice:23
msgid ""
"A tuple specifying the number of unit cells to generate in each lattice "
"vector direction (e.g., (Nx, Ny))."
msgstr ""

#: of tensorcircuit.templates.lattice.TILattice:26
msgid ""
"Specifies whether periodic boundary conditions are applied. Can be a "
"single boolean for all dimensions or a tuple of booleans for each "
"dimension individually. Defaults to True."
msgstr ""

#: of tensorcircuit.templates.lattice.TriangularLattice:1
msgid "A 2D triangular lattice."
msgstr ""

#: of tensorcircuit.templates.lattice.TriangularLattice:3
msgid "This is a Bravais lattice where each site has 6 nearest neighbors."
msgstr ""

#: of tensorcircuit.templates.lattice.TriangularLattice.__init__:1
msgid "Initializes the TriangularLattice."
msgstr ""

#: of tensorcircuit.templates.lattice.get_compatible_layers:1
msgid ""
"Partitions a list of pairs (bonds) into compatible layers for parallel "
"gate application using a greedy edge-coloring algorithm."
msgstr ""

#: of tensorcircuit.templates.lattice.get_compatible_layers:4
msgid ""
"This function takes a list of pairs, representing connections like "
"nearest-neighbor (NN) or next-nearest-neighbor (NNN) bonds, and "
"partitions them into the minimum number of sets (\"layers\") where no two"
" pairs in a set share an index. This is a general utility for scheduling "
"non-overlapping operations."
msgstr ""

#: of tensorcircuit.templates.lattice.get_compatible_layers:20
msgid ""
"A list of tuples, where each tuple represents a bond (i, j) of site "
"indices to be scheduled."
msgstr ""

#: of tensorcircuit.templates.lattice.get_compatible_layers:23
msgid ""
"A list of layers. Each layer is a list of tuples, where each tuple "
"represents a bond. All bonds within a layer are non-overlapping."
msgstr ""

#: ../../source/api/templates/measurements.rst:2
msgid "tensorcircuit.templates.measurements"
msgstr ""

#: of tensorcircuit.templates.measurements.any_local_measurements:1
msgid ""
"This measurements pattern is specifically suitable for vmap. Parameterize"
" the local Pauli string to be measured."
msgstr ""

#: of tensorcircuit.templates.measurements.any_local_measurements:19
#: tensorcircuit.templates.measurements.any_measurements:26
msgid "The circuit to be measured"
msgstr ""

#: of tensorcircuit.templates.measurements.any_local_measurements:21
#: tensorcircuit.templates.measurements.any_measurements:28
msgid ""
"parameter tensors determines what Pauli string to be measured, shape is "
"[nwires, 4] if ``onehot`` is False and [nwires] if ``onehot`` is True."
msgstr ""

#: of tensorcircuit.templates.measurements.any_local_measurements:24
#: tensorcircuit.templates.measurements.any_measurements:31
msgid ""
"defaults to False. If set to be True, structures will first go through "
"onehot procedure."
msgstr ""

#: of tensorcircuit.templates.measurements.any_local_measurements:27
msgid ""
"reuse the wavefunction when computing the expectations, defaults to be "
"True"
msgstr ""

#: of tensorcircuit.templates.measurements.any_local_measurements:29
#: tensorcircuit.templates.measurements.any_measurements:36
msgid "The expectation value of given Pauli string by the tensor ``structures``."
msgstr ""

#: of tensorcircuit.templates.measurements.any_measurements:1
msgid ""
"This measurements pattern is specifically suitable for vmap. Parameterize"
" the Pauli string to be measured."
msgstr ""

#: of tensorcircuit.templates.measurements.any_measurements:34
msgid ""
"reuse the wavefunction when computing the expectations, defaults to be "
"False"
msgstr ""

#: of tensorcircuit.templates.measurements.heisenberg_measurements:1
msgid ""
"Evaluate Heisenberg energy expectation, whose Hamiltonian is defined on "
"the lattice graph ``g`` as follows: (e are edges in graph ``g`` where e1 "
"and e2 are two nodes for edge e and v are nodes in graph ``g``)"
msgstr ""

#: of tensorcircuit.templates.measurements.heisenberg_measurements:4
#, python-brace-format
msgid ""
"H = \\sum_{e\\in g} w_e (h_{xx} X_{e1}X_{e2} + h_{yy} Y_{e1}Y_{e2} + "
"h_{zz} Z_{e1}Z_{e2})\n"
" + \\sum_{v\\in g} (h_x X_v + h_y Y_v + h_z Z_v)"
msgstr ""

#: of tensorcircuit.templates.measurements.heisenberg_measurements:18
msgid "Circuit to be measured"
msgstr ""

#: of tensorcircuit.templates.measurements.heisenberg_measurements:20
msgid "Lattice graph defining Heisenberg Hamiltonian"
msgstr ""

#: of tensorcircuit.templates.measurements.heisenberg_measurements:22
#: tensorcircuit.templates.measurements.heisenberg_measurements:24
#: tensorcircuit.templates.measurements.heisenberg_measurements:26
msgid "[description], defaults to 1.0"
msgstr ""

#: of tensorcircuit.templates.measurements.heisenberg_measurements:28
#: tensorcircuit.templates.measurements.heisenberg_measurements:30
#: tensorcircuit.templates.measurements.heisenberg_measurements:32
msgid "[description], defaults to 0.0"
msgstr ""

#: of tensorcircuit.templates.measurements.heisenberg_measurements:36
msgid "Value of Heisenberg energy"
msgstr ""

#: of tensorcircuit.templates.measurements.mpo_expectation:1
msgid ""
"Evaluate expectation of operator ``mpo`` defined in ``QuOperator`` MPO "
"format with the output quantum state from circuit ``c``."
msgstr ""

#: of tensorcircuit.templates.measurements.mpo_expectation:4
msgid "The circuit for the output state"
msgstr ""

#: of tensorcircuit.templates.measurements.mpo_expectation:6
msgid "MPO operator"
msgstr ""

#: of tensorcircuit.templates.measurements.mpo_expectation:8
#: tensorcircuit.templates.measurements.operator_expectation:7
#: tensorcircuit.templates.measurements.sparse_expectation:7
msgid "a real and scalar tensor of shape [] as the expectation value"
msgstr ""

#: of tensorcircuit.templates.measurements.operator_expectation:1
msgid ""
"Evaluate Hamiltonian expectation where ``hamiltonian`` can be dense "
"matrix, sparse matrix or MPO."
msgstr ""

#: of tensorcircuit.templates.measurements.operator_expectation:3
#: tensorcircuit.templates.measurements.sparse_expectation:3
msgid "The circuit whose output state is used to evaluate the expectation"
msgstr ""

#: of tensorcircuit.templates.measurements.operator_expectation:5
#: tensorcircuit.templates.measurements.sparse_expectation:5
msgid "Hamiltonian matrix in COO_sparse_matrix form"
msgstr ""

#: of tensorcircuit.templates.measurements.sparse_expectation:1
msgid ""
"Evaluate Hamiltonian expectation where ``hamiltonian`` is kept in sparse "
"matrix form to save space"
msgstr ""

#: of tensorcircuit.templates.measurements.spin_glass_measurements:1
msgid ""
"Compute spin glass energy defined on graph ``g`` expectation for output "
"state of the circuit ``c``. The Hamiltonian to be evaluated is defined as"
" (first term is determined by node weights while the second term is "
"determined by edge weights of the graph):"
msgstr ""

#: of tensorcircuit.templates.measurements.spin_glass_measurements:5
#, python-brace-format
msgid "H = \\sum_{v\\in g} w_v Z_v + \\sum_{e\\in g} w_e Z_{e1} Z_{e2}"
msgstr ""

#: of tensorcircuit.templates.measurements.spin_glass_measurements:28
msgid "The quantum circuit"
msgstr ""

#: of tensorcircuit.templates.measurements.spin_glass_measurements:30
msgid "The graph for spin glass Hamiltonian definition"
msgstr ""

#: of tensorcircuit.templates.measurements.spin_glass_measurements:32
msgid ""
"Whether measure the circuit with reusing the wavefunction, defaults to "
"True"
msgstr ""

#: of tensorcircuit.templates.measurements.spin_glass_measurements:34
msgid "The spin glass energy expectation value"
msgstr ""

#: ../../source/api/timeevol.rst:2
msgid "tensorcircuit.timeevol"
msgstr ""

#: of tensorcircuit.timeevol:1
msgid "Analog time evolution engines"
msgstr ""

#: of tensorcircuit.timeevol.chebyshev_evol:1
msgid ""
"Chebyshev evolution method by expanding the time evolution exponential "
"operator in Chebyshev series. Note the state returned is not normalized. "
"But the norm should be very close to 1 for sufficiently large k and M, "
"which can serve as a accuracy check of the final result."
msgstr ""

#: of tensorcircuit.timeevol.chebyshev_evol:6
msgid "Hamiltonian matrix (sparse or dense)"
msgstr ""

#: of tensorcircuit.timeevol.chebyshev_evol:8
#: tensorcircuit.timeevol.hamiltonian_evol:8
msgid "Initial state vector"
msgstr ""

#: of tensorcircuit.timeevol.chebyshev_evol:10
msgid "Time to evolve"
msgstr ""

#: of tensorcircuit.timeevol.chebyshev_evol:12
msgid "Spectral bounds for the Hamiltonian (Emax, Emin)"
msgstr ""

#: of tensorcircuit.timeevol.chebyshev_evol:14
msgid "Number of Chebyshev coefficients, a good estimate is k > t*(Emax-Emin)/2"
msgstr ""

#: of tensorcircuit.timeevol.chebyshev_evol:16
msgid ""
"Number of iterations to estimate Bessel function, a good estimate is "
"given by `estimate_M` helper method."
msgstr ""

#: of tensorcircuit.timeevol.chebyshev_evol:19
msgid "Evolved state"
msgstr ""

#: of tensorcircuit.timeevol.hamiltonian_evol:1
msgid ""
"Fast implementation of time independent Hamiltonian evolution using "
"eigendecomposition. By default, performs imaginary time evolution."
msgstr ""

#: of tensorcircuit.timeevol.hamiltonian_evol:4
msgid "Time-independent Hamiltonian matrix"
msgstr ""

#: of tensorcircuit.timeevol.hamiltonian_evol:6
msgid "alias for the argument ``h``"
msgstr ""

#: of tensorcircuit.timeevol.hamiltonian_evol:10
msgid "alias for the argument ``psi0``"
msgstr ""

#: of tensorcircuit.timeevol.hamiltonian_evol:12
msgid "Time points for evolution"
msgstr ""

#: of tensorcircuit.timeevol.hamiltonian_evol:14
msgid "alias for the argument ``tlist``"
msgstr ""

#: of tensorcircuit.timeevol.hamiltonian_evol:16
msgid "Optional function to process state at each time point"
msgstr ""

#: of tensorcircuit.timeevol.hamiltonian_evol:18
msgid ""
"Evolution results at each time point. If callback is None, returns state "
"vectors; otherwise returns callback results"
msgstr ""

#: of tensorcircuit.timeevol.hamiltonian_evol:44
msgid "The Hamiltonian must be time-independent"
msgstr ""

#: of tensorcircuit.timeevol.hamiltonian_evol:45
msgid ""
"For time-dependent Hamiltonians, use ``evol_local`` or ``evol_global`` "
"instead"
msgstr ""

#: of tensorcircuit.timeevol.hamiltonian_evol:46
msgid ""
"The evolution is performed in imaginary time by default (factor -t in "
"exponential)"
msgstr ""

#: of tensorcircuit.timeevol.hamiltonian_evol:47
msgid "The state is automatically normalized at each time point"
msgstr ""

#: of tensorcircuit.timeevol.estimate_M:1
msgid "estimate M for Bessel function iterations"
msgstr ""

#: of tensorcircuit.timeevol.estimate_M:3 tensorcircuit.timeevol.estimate_k:3
msgid "time"
msgstr ""

#: of tensorcircuit.timeevol.estimate_M:5 tensorcircuit.timeevol.estimate_k:5
msgid "spectral bounds (Emax, Emin)"
msgstr ""

#: of tensorcircuit.timeevol.estimate_M:7 tensorcircuit.timeevol.estimate_k:7
msgid "k"
msgstr ""

#: of tensorcircuit.timeevol.estimate_M:9
msgid "M"
msgstr ""

#: of tensorcircuit.timeevol.estimate_k:1
msgid "estimate k for chebyshev expansion"
msgstr ""

#: of tensorcircuit.timeevol.estimate_spectral_bounds:1
msgid ""
"Lanczos algorithm to estimate the spectral bounds of a Hamiltonian. Just "
"for quick run before `chebyshev_evol`, non jit-able."
msgstr ""

#: of tensorcircuit.timeevol.estimate_spectral_bounds:4
msgid "Hamiltonian matrix."
msgstr ""

#: of tensorcircuit.timeevol.estimate_spectral_bounds:6
msgid "iteration number."
msgstr ""

#: of tensorcircuit.timeevol.estimate_spectral_bounds:8
msgid "Optional initial state."
msgstr ""

#: of tensorcircuit.timeevol.estimate_spectral_bounds:10
msgid "(E_max, E_min)"
msgstr ""

#: of tensorcircuit.timeevol.evol_global:1
msgid ""
"ode evolution of time dependent Hamiltonian on circuit of all qubits "
"[only jax backend support for now]"
msgstr ""

#: of tensorcircuit.timeevol.evol_global:6
msgid ""
"h_fun should return a **SPARSE** Hamiltonian matrix with input arguments "
"``time`` and ``*args``"
msgstr ""

#: of tensorcircuit.timeevol.evol_global:9 tensorcircuit.timeevol.evol_local:11
msgid "alias for the argument ``h_fun``"
msgstr ""

#: of tensorcircuit.timeevol.evol_global:13
#: tensorcircuit.timeevol.evol_local:15
msgid "alias for the argument ``t``"
msgstr ""

#: of tensorcircuit.timeevol.evol_local:1
msgid ""
"ode evolution of time dependent Hamiltonian on circuit of given indices "
"[only jax backend support for now]"
msgstr ""

#: of tensorcircuit.timeevol.evol_local:6
msgid "qubit sites to evolve"
msgstr ""

#: of tensorcircuit.timeevol.evol_local:8
msgid ""
"h_fun should return a dense Hamiltonian matrix with input arguments "
"``time`` and ``*args``"
msgstr ""

#: of tensorcircuit.timeevol.evol_local:13
msgid "evolution time"
msgstr ""

#: of tensorcircuit.timeevol.krylov_evol:1
msgid "Perform quantum state time evolution using Krylov subspace method."
msgstr ""

#: of tensorcircuit.timeevol.krylov_evol:3
#: tensorcircuit.timeevol.lanczos_iteration:4
#: tensorcircuit.timeevol.lanczos_iteration_scan:4
msgid "Sparse or dense Hamiltonian matrix"
msgstr ""

#: of tensorcircuit.timeevol.krylov_evol:5
msgid "Initial quantum state"
msgstr ""

#: of tensorcircuit.timeevol.krylov_evol:7
msgid "List of time points"
msgstr ""

#: of tensorcircuit.timeevol.krylov_evol:9
msgid "Krylov subspace dimension"
msgstr ""

#: of tensorcircuit.timeevol.krylov_evol:11
msgid ""
"Optional callback function applied to quantum state at each evolution "
"time point, return some observables"
msgstr ""

#: of tensorcircuit.timeevol.krylov_evol:14
msgid ""
"whether use scan implementation, suitable for jit but may be slow on "
"numpy defaults False, True not work for tensorflow backend + jit, due to "
"stupid issue of tensorflow context separation and the notorious "
"inaccesibletensor error"
msgstr ""

#: of tensorcircuit.timeevol.krylov_evol:18
msgid ""
"List of evolved quantum states, or list of callback function results (if "
"callback provided)"
msgstr ""

#: of tensorcircuit.timeevol.lanczos_iteration:1
msgid ""
"Use Lanczos algorithm to construct orthogonal basis and projected "
"Hamiltonian of Krylov subspace."
msgstr ""

#: of tensorcircuit.timeevol.lanczos_iteration:6
#: tensorcircuit.timeevol.lanczos_iteration_scan:6
msgid "Initial quantum state vector"
msgstr ""

#: of tensorcircuit.timeevol.lanczos_iteration:8
#: tensorcircuit.timeevol.lanczos_iteration_scan:8
msgid "Dimension of Krylov subspace"
msgstr ""

#: of tensorcircuit.timeevol.lanczos_iteration:10
#: tensorcircuit.timeevol.lanczos_iteration_scan:10
msgid "Tuple containing (basis matrix, projected Hamiltonian)"
msgstr ""

#: of tensorcircuit.timeevol.lanczos_iteration_scan:1
msgid ""
"Use Lanczos algorithm to construct orthogonal basis and projected "
"Hamiltonian of Krylov subspace, using `tc.backend.scan` for JIT "
"compatibility."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_global:1
msgid ""
"ODE-based time evolution for a time-dependent Hamiltonian acting on the "
"entire system. This function solves the time-dependent Schrodinger "
"equation using numerical ODE integration. The Hamiltonian is applied to "
"the full system and should be provided in sparse matrix format for "
"efficiency. The ode_backend parameter defaults to 'jaxode' (which uses "
"``jax.experimental.ode.odeint`` with a default solver of 'Dopri5'); if "
"set to 'diffrax', it uses ``diffrax.diffeqsolve`` instead (with a default"
" solver of 'Tsit5')."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_global:8
#: tensorcircuit.timeevol.ode_evol_local:7
msgid "Note: This function currently only supports the JAX backend."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_global:10
msgid ""
"A function that returns a sparse Hamiltonian matrix for the full system. "
"The function signature should be ``hamiltonian(time, *args) -> Tensor``."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_global:13
msgid "The initial quantum state vector."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_global:15
#: tensorcircuit.timeevol.ode_evol_local:14
msgid ""
"Time points for which to compute the evolution. Should be a 1D array of "
"times."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_global:17
#: tensorcircuit.timeevol.ode_evol_local:18
msgid "Optional function to apply to the state at each time step."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_global:19
#: tensorcircuit.timeevol.ode_evol_local:20
msgid "Additional arguments to pass to the Hamiltonian function."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_global:21
#: tensorcircuit.timeevol.ode_evol_local:21
#, python-brace-format
msgid ""
"Additional keyword arguments to pass to the ODE solver.  - "
"``ode_backend='jaxode'`` (default) uses ``jax.experimental.ode.odeint``; "
"``ode_backend='diffrax'``   uses ``diffrax.diffeqsolve``.  - ``rtol`` "
"(default: 1e-8) and ``atol`` (default: 1e-8) are used to determine how "
"accurately you would   like the numerical approximation to your equation."
"  - The ``solver`` parameter accepts one of {'Tsit5' (default), 'Dopri5',"
" 'Dopri8', 'Kvaerno5'}   and only works when ``ode_backend='diffrax'``.  "
"- ``t0`` (default: 0.01) specifies the initial step size and only works "
"when ``ode_backend='diffrax'``.  - ``max_steps`` (default: 4096)  The "
"maximum number of steps to take before quitting the computation   "
"unconditionally and only works when ``ode_backend='diffrax'``."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_global:21
#: tensorcircuit.timeevol.ode_evol_local:21
msgid "Additional keyword arguments to pass to the ODE solver."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_global:23
#: tensorcircuit.timeevol.ode_evol_local:23
msgid ""
"``ode_backend='jaxode'`` (default) uses ``jax.experimental.ode.odeint``; "
"``ode_backend='diffrax'`` uses ``diffrax.diffeqsolve``."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_global:26
#: tensorcircuit.timeevol.ode_evol_local:26
msgid ""
"``rtol`` (default: 1e-8) and ``atol`` (default: 1e-8) are used to "
"determine how accurately you would like the numerical approximation to "
"your equation."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_global:29
#: tensorcircuit.timeevol.ode_evol_local:29
#, python-brace-format
msgid ""
"The ``solver`` parameter accepts one of {'Tsit5' (default), 'Dopri5', "
"'Dopri8', 'Kvaerno5'} and only works when ``ode_backend='diffrax'``."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_global:32
#: tensorcircuit.timeevol.ode_evol_local:32
msgid ""
"``t0`` (default: 0.01) specifies the initial step size and only works "
"when ``ode_backend='diffrax'``."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_global:34
#: tensorcircuit.timeevol.ode_evol_local:34
msgid ""
"``max_steps`` (default: 4096)  The maximum number of steps to take before"
" quitting the computation unconditionally and only works when "
"``ode_backend='diffrax'``."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_global:38
#: tensorcircuit.timeevol.ode_evol_local:38
msgid ""
"Evolved quantum states at the specified time points. If callback is "
"provided, returns the callback results; otherwise returns the state "
"vectors."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_local:1
msgid ""
"ODE-based time evolution for a time-dependent Hamiltonian acting on a "
"subsystem of qubits. This function solves the time-dependent Schrodinger "
"equation using numerical ODE integration. The Hamiltonian is applied only"
" to a specific subset of qubits (indices) in the system. The ode_backend "
"parameter defaults to 'jaxode' (which uses "
"``jax.experimental.ode.odeint`` with a default solver of 'Dopri5'); if "
"set to 'diffrax', it uses ``diffrax.diffeqsolve`` instead (with a default"
" solver of 'Tsit5')."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_local:9
msgid ""
"A function that returns a dense Hamiltonian matrix for the specified "
"subsystem size. The function signature should be ``hamiltonian(time, "
"*args) -> Tensor``."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_local:12
msgid "The initial quantum state vector of the full system."
msgstr ""

#: of tensorcircuit.timeevol.ode_evol_local:16
msgid "Indices of qubits where the Hamiltonian is applied."
msgstr ""

#: ../../source/api/torchnn.rst:2
msgid "tensorcircuit.torchnn"
msgstr ""

#: of tensorcircuit.torchnn:1
msgid "PyTorch nn Module wrapper for quantum function"
msgstr ""

#: of tensorcircuit.torchnn.HardwareNet:1
msgid ""
"PyTorch Layer wrapping quantum function with cloud qpu access (using "
":py:mod:`tensorcircuit.cloud` module)"
msgstr ""

#: of tensorcircuit.torchnn.HardwareNet.__init__:1
#: tensorcircuit.torchnn.QuantumNet.__init__:1
msgid "PyTorch nn Module wrapper on quantum function ``f``."
msgstr ""

#: of tensorcircuit.torchnn.HardwareNet.__init__:32
#: tensorcircuit.torchnn.QuantumNet.__init__:32
msgid "Quantum function with tensor in (input and weights) and tensor out."
msgstr ""

#: of tensorcircuit.torchnn.HardwareNet.__init__:34
#: tensorcircuit.torchnn.QuantumNet.__init__:34
msgid ""
"list of shape tuple for different weights as the non-first parameters for"
" ``f``"
msgstr ""

#: of tensorcircuit.torchnn.HardwareNet.__init__:36
#: tensorcircuit.torchnn.QuantumNet.__init__:36
msgid "function that gives the shape tuple returns torch tensor, defaults to None"
msgstr ""

#: of tensorcircuit.torchnn.HardwareNet.__init__:38
#: tensorcircuit.torchnn.QuantumNet.__init__:38
msgid "whether apply vmap (batch input) on ``f``, defaults to True"
msgstr ""

#: of tensorcircuit.torchnn.HardwareNet.__init__:40
#: tensorcircuit.torchnn.QuantumNet.__init__:40
msgid ""
"which position of input should be batched, need to be customized when "
"multiple inputs for the torch model, defaults to be 0."
msgstr ""

#: of tensorcircuit.torchnn.HardwareNet.__init__:43
#: tensorcircuit.torchnn.QuantumNet.__init__:43
msgid "whether transform ``f`` with torch interface, defaults to True"
msgstr ""

#: of tensorcircuit.torchnn.HardwareNet.__init__:45
#: tensorcircuit.torchnn.QuantumNet.__init__:45
msgid "whether jit ``f``, defaults to True"
msgstr ""

#: of tensorcircuit.torchnn.HardwareNet.__init__:47
#: tensorcircuit.torchnn.QuantumNet.__init__:47
msgid "whether enbale dlpack in interfaces, defaults to False"
msgstr ""

#: of torch.nn.modules.module.Module.add_module:1
msgid "Add a child module to the current module."
msgstr ""

#: of torch.nn.modules.module.Module.add_module:3
msgid "The module can be accessed as an attribute using the given name."
msgstr ""

#: of torch.nn.modules.module.Module.add_module:5
msgid ""
"name of the child module. The child module can be accessed from this "
"module using the given name"
msgstr ""

#: of torch.nn.modules.module.Module.add_module:8
msgid "child module to be added to the module."
msgstr ""

#: of torch.nn.modules.module.Module.apply:1
msgid ""
"Apply ``fn`` recursively to every submodule (as returned by "
"``.children()``) as well as self."
msgstr ""

#: of torch.nn.modules.module.Module.apply:3
msgid ""
"Typical use includes initializing the parameters of a model (see also "
":ref:`nn-init-doc`)."
msgstr ""

#: of torch.nn.modules.module.Module.apply:6
msgid "function to be applied to each submodule"
msgstr ""

#: of torch.nn.modules.module.Module.apply:9
#: torch.nn.modules.module.Module.bfloat16:6
#: torch.nn.modules.module.Module.cpu:6 torch.nn.modules.module.Module.cuda:14
#: torch.nn.modules.module.Module.double:6
#: torch.nn.modules.module.Module.eval:13
#: torch.nn.modules.module.Module.float:6 torch.nn.modules.module.Module.half:6
#: torch.nn.modules.module.Module.ipu:14
#: torch.nn.modules.module.Module.requires_grad_:16
#: torch.nn.modules.module.Module.to:45
#: torch.nn.modules.module.Module.to_empty:10
#: torch.nn.modules.module.Module.train:12
#: torch.nn.modules.module.Module.type:9 torch.nn.modules.module.Module.xpu:14
msgid "self"
msgstr ""

#: of torch.nn.modules.module.Module.bfloat16:1
msgid "Casts all floating point parameters and buffers to ``bfloat16`` datatype."
msgstr ""

#: of torch.nn.modules.module.Module.bfloat16:4
#: torch.nn.modules.module.Module.cpu:4 torch.nn.modules.module.Module.cuda:8
#: torch.nn.modules.module.Module.double:4
#: torch.nn.modules.module.Module.float:4 torch.nn.modules.module.Module.half:4
#: torch.nn.modules.module.Module.ipu:8 torch.nn.modules.module.Module.to:29
#: torch.nn.modules.module.Module.type:4 torch.nn.modules.module.Module.xpu:8
msgid "This method modifies the module in-place."
msgstr ""

#: of torch.nn.modules.module.Module.buffers:1
msgid "Return an iterator over module buffers."
msgstr ""

#: of torch.nn.modules.module.Module.buffers:3
msgid ""
"if True, then yields buffers of this module and all submodules. "
"Otherwise, yields only buffers that are direct members of this module."
msgstr ""

#: of torch.nn.modules.module.Module.buffers
#: torch.nn.modules.module.Module.children
#: torch.nn.modules.module.Module.modules
#: torch.nn.modules.module.Module.named_buffers
#: torch.nn.modules.module.Module.named_children
#: torch.nn.modules.module.Module.named_modules
#: torch.nn.modules.module.Module.named_parameters
#: torch.nn.modules.module.Module.parameters
msgid "Yields"
msgstr ""

#: of torch.nn.modules.module.Module.buffers:8
msgid "*torch.Tensor* -- module buffer"
msgstr ""

#: of torch.nn.modules.module.Module.children:1
msgid "Return an iterator over immediate children modules."
msgstr ""

#: of torch.nn.modules.module.Module.children:3
msgid "*Module* -- a child module"
msgstr ""

#: of torch.nn.modules.module.Module.compile:1
msgid "Compile this Module's forward using :func:`torch.compile`."
msgstr ""

#: of torch.nn.modules.module.Module.compile:3
msgid ""
"This Module's `__call__` method is compiled and all arguments are passed "
"as-is to :func:`torch.compile`."
msgstr ""

#: of torch.nn.modules.module.Module.compile:6
msgid "See :func:`torch.compile` for details on the arguments for this function."
msgstr ""

#: of torch.nn.modules.module.Module.cpu:1
msgid "Move all model parameters and buffers to the CPU."
msgstr ""

#: of torch.nn.modules.module.Module.cuda:1
msgid "Move all model parameters and buffers to the GPU."
msgstr ""

#: of torch.nn.modules.module.Module.cuda:3
msgid ""
"This also makes associated parameters and buffers different objects. So "
"it should be called before constructing optimizer if the module will live"
" on GPU while being optimized."
msgstr ""

#: of torch.nn.modules.module.Module.cuda:10
#: torch.nn.modules.module.Module.ipu:10 torch.nn.modules.module.Module.xpu:10
msgid "if specified, all parameters will be copied to that device"
msgstr ""

#: of torch.nn.modules.module.Module.double:1
msgid "Casts all floating point parameters and buffers to ``double`` datatype."
msgstr ""

#: of torch.nn.modules.module.Module.eval:1
msgid "Set the module in evaluation mode."
msgstr ""

#: of torch.nn.modules.module.Module.eval:3
#: torch.nn.modules.module.Module.train:3
msgid ""
"This has any effect only on certain modules. See documentations of "
"particular modules for details of their behaviors in training/evaluation "
"mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`, "
"etc."
msgstr ""

#: of torch.nn.modules.module.Module.eval:8
msgid "This is equivalent with :meth:`self.train(False) <torch.nn.Module.train>`."
msgstr ""

#: of torch.nn.modules.module.Module.eval:10
msgid ""
"See :ref:`locally-disable-grad-doc` for a comparison between `.eval()` "
"and several similar mechanisms that may be confused with it."
msgstr ""

#: of torch.nn.modules.module.Module.extra_repr:1
msgid "Set the extra representation of the module."
msgstr ""

#: of torch.nn.modules.module.Module.extra_repr:3
msgid ""
"To print customized extra information, you should re-implement this "
"method in your own modules. Both single-line and multi-line strings are "
"acceptable."
msgstr ""

#: of torch.nn.modules.module.Module.float:1
msgid "Casts all floating point parameters and buffers to ``float`` datatype."
msgstr ""

#: of tensorcircuit.torchnn.HardwareNet.forward:1
#: tensorcircuit.torchnn.QuantumNet.forward:1
msgid "Define the computation performed at every call."
msgstr ""

#: of tensorcircuit.torchnn.HardwareNet.forward:3
#: tensorcircuit.torchnn.QuantumNet.forward:3
msgid "Should be overridden by all subclasses."
msgstr ""

#: of tensorcircuit.torchnn.HardwareNet.forward:6
#: tensorcircuit.torchnn.QuantumNet.forward:6
msgid ""
"Although the recipe for forward pass needs to be defined within this "
"function, one should call the :class:`Module` instance afterwards instead"
" of this since the former takes care of running the registered hooks "
"while the latter silently ignores them."
msgstr ""

#: of torch.nn.modules.module.Module.get_buffer:1
msgid ""
"Return the buffer given by ``target`` if it exists, otherwise throw an "
"error."
msgstr ""

#: of torch.nn.modules.module.Module.get_buffer:3
#: torch.nn.modules.module.Module.get_parameter:3
msgid ""
"See the docstring for ``get_submodule`` for a more detailed explanation "
"of this method's functionality as well as how to correctly specify "
"``target``."
msgstr ""

#: of torch.nn.modules.module.Module.get_buffer:7
msgid ""
"The fully-qualified string name of the buffer to look for. (See "
"``get_submodule`` for how to specify a fully-qualified string.)"
msgstr ""

#: of torch.nn.modules.module.Module.get_buffer:11
msgid "The buffer referenced by ``target``"
msgstr ""

#: of torch.nn.modules.module.Module.get_buffer:14
msgid ""
"If the target string references an invalid     path or resolves to "
"something that is not a     buffer"
msgstr ""

#: of torch.nn.modules.module.Module.get_extra_state:1
msgid "Return any extra state to include in the module's state_dict."
msgstr ""

#: of torch.nn.modules.module.Module.get_extra_state:3
msgid ""
"Implement this and a corresponding :func:`set_extra_state` for your "
"module if you need to store extra state. This function is called when "
"building the module's `state_dict()`."
msgstr ""

#: of torch.nn.modules.module.Module.get_extra_state:7
msgid ""
"Note that extra state should be picklable to ensure working serialization"
" of the state_dict. We only provide provide backwards compatibility "
"guarantees for serializing Tensors; other objects may break backwards "
"compatibility if their serialized pickled form changes."
msgstr ""

#: of torch.nn.modules.module.Module.get_extra_state:12
msgid "Any extra state to store in the module's state_dict"
msgstr ""

#: of torch.nn.modules.module.Module.get_parameter:1
msgid ""
"Return the parameter given by ``target`` if it exists, otherwise throw an"
" error."
msgstr ""

#: of torch.nn.modules.module.Module.get_parameter:7
msgid ""
"The fully-qualified string name of the Parameter to look for. (See "
"``get_submodule`` for how to specify a fully-qualified string.)"
msgstr ""

#: of torch.nn.modules.module.Module.get_parameter:11
msgid "The Parameter referenced by ``target``"
msgstr ""

#: of torch.nn.modules.module.Module.get_parameter:14
msgid ""
"If the target string references an invalid     path or resolves to "
"something that is not an     ``nn.Parameter``"
msgstr ""

#: of torch.nn.modules.module.Module.get_submodule:1
msgid ""
"Return the submodule given by ``target`` if it exists, otherwise throw an"
" error."
msgstr ""

#: of torch.nn.modules.module.Module.get_submodule:3
msgid ""
"For example, let's say you have an ``nn.Module`` ``A`` that looks like "
"this:"
msgstr ""

#: of torch.nn.modules.module.Module.get_submodule:17
msgid ""
"(The diagram shows an ``nn.Module`` ``A``. ``A`` has a nested submodule "
"``net_b``, which itself has two submodules ``net_c`` and ``linear``. "
"``net_c`` then has a submodule ``conv``.)"
msgstr ""

#: of torch.nn.modules.module.Module.get_submodule:21
msgid ""
"To check whether or not we have the ``linear`` submodule, we would call "
"``get_submodule(\"net_b.linear\")``. To check whether we have the "
"``conv`` submodule, we would call "
"``get_submodule(\"net_b.net_c.conv\")``."
msgstr ""

#: of torch.nn.modules.module.Module.get_submodule:26
msgid ""
"The runtime of ``get_submodule`` is bounded by the degree of module "
"nesting in ``target``. A query against ``named_modules`` achieves the "
"same result, but it is O(N) in the number of transitive modules. So, for "
"a simple check to see if some submodule exists, ``get_submodule`` should "
"always be used."
msgstr ""

#: of torch.nn.modules.module.Module.get_submodule:33
msgid ""
"The fully-qualified string name of the submodule to look for. (See above "
"example for how to specify a fully-qualified string.)"
msgstr ""

#: of torch.nn.modules.module.Module.get_submodule:37
msgid "The submodule referenced by ``target``"
msgstr ""

#: of torch.nn.modules.module.Module.get_submodule:40
msgid ""
"If the target string references an invalid     path or resolves to "
"something that is not an     ``nn.Module``"
msgstr ""

#: of torch.nn.modules.module.Module.half:1
msgid "Casts all floating point parameters and buffers to ``half`` datatype."
msgstr ""

#: of torch.nn.modules.module.Module.ipu:1
msgid "Move all model parameters and buffers to the IPU."
msgstr ""

#: of torch.nn.modules.module.Module.ipu:3
msgid ""
"This also makes associated parameters and buffers different objects. So "
"it should be called before constructing optimizer if the module will live"
" on IPU while being optimized."
msgstr ""

#: of torch.nn.modules.module.Module.load_state_dict:1
msgid ""
"Copy parameters and buffers from :attr:`state_dict` into this module and "
"its descendants."
msgstr ""

#: of torch.nn.modules.module.Module.load_state_dict:3
msgid ""
"If :attr:`strict` is ``True``, then the keys of :attr:`state_dict` must "
"exactly match the keys returned by this module's "
":meth:`~torch.nn.Module.state_dict` function."
msgstr ""

#: of torch.nn.modules.module.Module.load_state_dict:8
msgid ""
"If :attr:`assign` is ``True`` the optimizer must be created after the "
"call to :attr:`load_state_dict`."
msgstr ""

#: of torch.nn.modules.module.Module.load_state_dict:11
msgid "a dict containing parameters and persistent buffers."
msgstr ""

#: of torch.nn.modules.module.Module.load_state_dict:14
msgid ""
"whether to strictly enforce that the keys in :attr:`state_dict` match the"
" keys returned by this module's :meth:`~torch.nn.Module.state_dict` "
"function. Default: ``True``"
msgstr ""

#: of torch.nn.modules.module.Module.load_state_dict:18
msgid ""
"whether to assign items in the state dictionary to their corresponding "
"keys in the module instead of copying them inplace into the module's "
"current parameters and buffers. When ``False``, the properties of the "
"tensors in the current module are preserved while when ``True``, the "
"properties of the Tensors in the state dict are preserved. Default: "
"``False``"
msgstr ""

#: of torch.nn.modules.module.Module.load_state_dict:27
msgid ""
"* **missing_keys** is a list of str containing the missing keys * "
"**unexpected_keys** is a list of str containing the unexpected keys"
msgstr ""

#: of torch.nn.modules.module.Module.load_state_dict:27
msgid "**missing_keys** is a list of str containing the missing keys"
msgstr ""

#: of torch.nn.modules.module.Module.load_state_dict:28
msgid "**unexpected_keys** is a list of str containing the unexpected keys"
msgstr ""

#: of torch.nn.modules.module.Module.load_state_dict:29
msgid "``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields"
msgstr ""

#: of torch.nn.modules.module.Module.load_state_dict:33
msgid ""
"If a parameter or buffer is registered as ``None`` and its corresponding "
"key exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a "
"``RuntimeError``."
msgstr ""

#: of torch.nn.modules.module.Module.modules:1
msgid "Return an iterator over all modules in the network."
msgstr ""

#: of torch.nn.modules.module.Module.modules:3
msgid "*Module* -- a module in the network"
msgstr ""

#: of torch.nn.modules.module.Module.modules:7
#: torch.nn.modules.module.Module.named_modules:12
msgid ""
"Duplicate modules are returned only once. In the following example, ``l``"
" will be returned only once."
msgstr ""

#: of torch.nn.modules.module.Module.named_buffers:1
msgid ""
"Return an iterator over module buffers, yielding both the name of the "
"buffer as well as the buffer itself."
msgstr ""

#: of torch.nn.modules.module.Module.named_buffers:3
msgid "prefix to prepend to all buffer names."
msgstr ""

#: of torch.nn.modules.module.Module.named_buffers:5
msgid ""
"if True, then yields buffers of this module and all submodules. "
"Otherwise, yields only buffers that are direct members of this module. "
"Defaults to True."
msgstr ""

#: of torch.nn.modules.module.Module.named_buffers:9
msgid "whether to remove the duplicated buffers in the result. Defaults to True."
msgstr ""

#: of torch.nn.modules.module.Module.named_buffers:12
msgid "*(str, torch.Tensor)* -- Tuple containing the name and buffer"
msgstr ""

#: of torch.nn.modules.module.Module.named_children:1
msgid ""
"Return an iterator over immediate children modules, yielding both the "
"name of the module as well as the module itself."
msgstr ""

#: of torch.nn.modules.module.Module.named_children:3
msgid "*(str, Module)* -- Tuple containing a name and child module"
msgstr ""

#: of torch.nn.modules.module.Module.named_modules:1
msgid ""
"Return an iterator over all modules in the network, yielding both the "
"name of the module as well as the module itself."
msgstr ""

#: of torch.nn.modules.module.Module.named_modules:3
msgid "a memo to store the set of modules already added to the result"
msgstr ""

#: of torch.nn.modules.module.Module.named_modules:4
msgid "a prefix that will be added to the name of the module"
msgstr ""

#: of torch.nn.modules.module.Module.named_modules:5
msgid "whether to remove the duplicated module instances in the result or not"
msgstr ""

#: of torch.nn.modules.module.Module.named_modules:8
msgid "*(str, Module)* -- Tuple of name and module"
msgstr ""

#: of torch.nn.modules.module.Module.named_parameters:1
msgid ""
"Return an iterator over module parameters, yielding both the name of the "
"parameter as well as the parameter itself."
msgstr ""

#: of torch.nn.modules.module.Module.named_parameters:3
msgid "prefix to prepend to all parameter names."
msgstr ""

#: of torch.nn.modules.module.Module.named_parameters:5
#: torch.nn.modules.module.Module.parameters:5
msgid ""
"if True, then yields parameters of this module and all submodules. "
"Otherwise, yields only parameters that are direct members of this module."
msgstr ""

#: of torch.nn.modules.module.Module.named_parameters:9
msgid ""
"whether to remove the duplicated parameters in the result. Defaults to "
"True."
msgstr ""

#: of torch.nn.modules.module.Module.named_parameters:13
msgid "*(str, Parameter)* -- Tuple containing the name and parameter"
msgstr ""

#: of torch.nn.modules.module.Module.parameters:1
msgid "Return an iterator over module parameters."
msgstr ""

#: of torch.nn.modules.module.Module.parameters:3
msgid "This is typically passed to an optimizer."
msgstr ""

#: of torch.nn.modules.module.Module.parameters:10
msgid "*Parameter* -- module parameter"
msgstr ""

#: of torch.nn.modules.module.Module.register_backward_hook:1
#: torch.nn.modules.module.Module.register_full_backward_hook:1
msgid "Register a backward hook on the module."
msgstr ""

#: of torch.nn.modules.module.Module.register_backward_hook:3
msgid ""
"This function is deprecated in favor of "
":meth:`~torch.nn.Module.register_full_backward_hook` and the behavior of "
"this function will change in future versions."
msgstr ""

#: of torch.nn.modules.module.Module.register_backward_hook:6
#: torch.nn.modules.module.Module.register_forward_hook:41
#: torch.nn.modules.module.Module.register_forward_pre_hook:40
#: torch.nn.modules.module.Module.register_full_backward_hook:39
#: torch.nn.modules.module.Module.register_full_backward_pre_hook:34
#: torch.nn.modules.module.Module.register_load_state_dict_post_hook:20
msgid ""
"a handle that can be used to remove the added hook by calling "
"``handle.remove()``"
msgstr ""

#: of torch.nn.modules.module.Module.register_backward_hook:8
#: torch.nn.modules.module.Module.register_forward_hook:43
#: torch.nn.modules.module.Module.register_forward_pre_hook:42
#: torch.nn.modules.module.Module.register_full_backward_hook:41
#: torch.nn.modules.module.Module.register_full_backward_pre_hook:36
#: torch.nn.modules.module.Module.register_load_state_dict_post_hook:22
msgid ":class:`torch.utils.hooks.RemovableHandle`"
msgstr ""

#: of torch.nn.modules.module.Module.register_buffer:1
msgid "Add a buffer to the module."
msgstr ""

#: of torch.nn.modules.module.Module.register_buffer:3
msgid ""
"This is typically used to register a buffer that should not to be "
"considered a model parameter. For example, BatchNorm's ``running_mean`` "
"is not a parameter, but is part of the module's state. Buffers, by "
"default, are persistent and will be saved alongside parameters. This "
"behavior can be changed by setting :attr:`persistent` to ``False``. The "
"only difference between a persistent buffer and a non-persistent buffer "
"is that the latter will not be a part of this module's "
":attr:`state_dict`."
msgstr ""

#: of torch.nn.modules.module.Module.register_buffer:12
msgid "Buffers can be accessed as attributes using given names."
msgstr ""

#: of torch.nn.modules.module.Module.register_buffer:14
msgid ""
"name of the buffer. The buffer can be accessed from this module using the"
" given name"
msgstr ""

#: of torch.nn.modules.module.Module.register_buffer:17
msgid ""
"buffer to be registered. If ``None``, then operations that run on "
"buffers, such as :attr:`cuda`, are ignored. If ``None``, the buffer is "
"**not** included in the module's :attr:`state_dict`."
msgstr ""

#: of torch.nn.modules.module.Module.register_buffer:21
msgid "whether the buffer is part of this module's :attr:`state_dict`."
msgstr ""

#: of torch.nn.modules.module.Module.register_forward_hook:1
msgid "Register a forward hook on the module."
msgstr ""

#: of torch.nn.modules.module.Module.register_forward_hook:3
msgid ""
"The hook will be called every time after :func:`forward` has computed an "
"output."
msgstr ""

#: of torch.nn.modules.module.Module.register_forward_hook:5
msgid ""
"If ``with_kwargs`` is ``False`` or not specified, the input contains only"
" the positional arguments given to the module. Keyword arguments won't be"
" passed to the hooks and only to the ``forward``. The hook can modify the"
" output. It can modify the input inplace but it will not have effect on "
"forward since this is called after :func:`forward` is called. The hook "
"should have the following signature::"
msgstr ""

#: of torch.nn.modules.module.Module.register_forward_hook:14
msgid ""
"If ``with_kwargs`` is ``True``, the forward hook will be passed the "
"``kwargs`` given to the forward function and be expected to return the "
"output possibly modified. The hook should have the following signature::"
msgstr ""

#: of torch.nn.modules.module.Module.register_forward_hook:20
#: torch.nn.modules.module.Module.register_forward_pre_hook:23
msgid "The user defined hook to be registered."
msgstr ""

#: of torch.nn.modules.module.Module.register_forward_hook:22
msgid ""
"If ``True``, the provided ``hook`` will be fired before all existing "
"``forward`` hooks on this :class:`torch.nn.modules.Module`. Otherwise, "
"the provided ``hook`` will be fired after all existing ``forward`` hooks "
"on this :class:`torch.nn.modules.Module`. Note that global ``forward`` "
"hooks registered with :func:`register_module_forward_hook` will fire "
"before all hooks registered by this method. Default: ``False``"
msgstr ""

#: of torch.nn.modules.module.Module.register_forward_hook:32
msgid ""
"If ``True``, the ``hook`` will be passed the kwargs given to the forward "
"function. Default: ``False``"
msgstr ""

#: of torch.nn.modules.module.Module.register_forward_hook:36
msgid ""
"If ``True`` the ``hook`` will be run regardless of whether an exception "
"is raised while calling the Module. Default: ``False``"
msgstr ""

#: of torch.nn.modules.module.Module.register_forward_pre_hook:1
msgid "Register a forward pre-hook on the module."
msgstr ""

#: of torch.nn.modules.module.Module.register_forward_pre_hook:3
msgid "The hook will be called every time before :func:`forward` is invoked."
msgstr ""

#: of torch.nn.modules.module.Module.register_forward_pre_hook:6
msgid ""
"If ``with_kwargs`` is false or not specified, the input contains only the"
" positional arguments given to the module. Keyword arguments won't be "
"passed to the hooks and only to the ``forward``. The hook can modify the "
"input. User can either return a tuple or a single modified value in the "
"hook. We will wrap the value into a tuple if a single value is returned "
"(unless that value is already a tuple). The hook should have the "
"following signature::"
msgstr ""

#: of torch.nn.modules.module.Module.register_forward_pre_hook:16
msgid ""
"If ``with_kwargs`` is true, the forward pre-hook will be passed the "
"kwargs given to the forward function. And if the hook modifies the input,"
" both the args and kwargs should be returned. The hook should have the "
"following signature::"
msgstr ""

#: of torch.nn.modules.module.Module.register_forward_pre_hook:25
msgid ""
"If true, the provided ``hook`` will be fired before all existing "
"``forward_pre`` hooks on this :class:`torch.nn.modules.Module`. "
"Otherwise, the provided ``hook`` will be fired after all existing "
"``forward_pre`` hooks on this :class:`torch.nn.modules.Module`. Note that"
" global ``forward_pre`` hooks registered with "
":func:`register_module_forward_pre_hook` will fire before all hooks "
"registered by this method. Default: ``False``"
msgstr ""

#: of torch.nn.modules.module.Module.register_forward_pre_hook:35
msgid ""
"If true, the ``hook`` will be passed the kwargs given to the forward "
"function. Default: ``False``"
msgstr ""

#: of torch.nn.modules.module.Module.register_full_backward_hook:3
msgid ""
"The hook will be called every time the gradients with respect to a module"
" are computed, i.e. the hook will execute if and only if the gradients "
"with respect to module outputs are computed. The hook should have the "
"following signature::"
msgstr ""

#: of torch.nn.modules.module.Module.register_full_backward_hook:10
msgid ""
"The :attr:`grad_input` and :attr:`grad_output` are tuples that contain "
"the gradients with respect to the inputs and outputs respectively. The "
"hook should not modify its arguments, but it can optionally return a new "
"gradient with respect to the input that will be used in place of "
":attr:`grad_input` in subsequent computations. :attr:`grad_input` will "
"only correspond to the inputs given as positional arguments and all kwarg"
" arguments are ignored. Entries in :attr:`grad_input` and "
":attr:`grad_output` will be ``None`` for all non-Tensor arguments."
msgstr ""

#: of torch.nn.modules.module.Module.register_full_backward_hook:19
#: torch.nn.modules.module.Module.register_full_backward_pre_hook:14
msgid ""
"For technical reasons, when this hook is applied to a Module, its forward"
" function will receive a view of each Tensor passed to the Module. "
"Similarly the caller will receive a view of each Tensor returned by the "
"Module's forward function."
msgstr ""

#: of torch.nn.modules.module.Module.register_full_backward_hook:24
msgid ""
"Modifying inputs or outputs inplace is not allowed when using backward "
"hooks and will raise an error."
msgstr ""

#: of torch.nn.modules.module.Module.register_full_backward_hook:27
#: torch.nn.modules.module.Module.register_full_backward_pre_hook:22
msgid "The user-defined hook to be registered."
msgstr ""

#: of torch.nn.modules.module.Module.register_full_backward_hook:29
msgid ""
"If true, the provided ``hook`` will be fired before all existing "
"``backward`` hooks on this :class:`torch.nn.modules.Module`. Otherwise, "
"the provided ``hook`` will be fired after all existing ``backward`` hooks"
" on this :class:`torch.nn.modules.Module`. Note that global ``backward`` "
"hooks registered with :func:`register_module_full_backward_hook` will "
"fire before all hooks registered by this method."
msgstr ""

#: of torch.nn.modules.module.Module.register_full_backward_pre_hook:1
msgid "Register a backward pre-hook on the module."
msgstr ""

#: of torch.nn.modules.module.Module.register_full_backward_pre_hook:3
msgid ""
"The hook will be called every time the gradients for the module are "
"computed. The hook should have the following signature::"
msgstr ""

#: of torch.nn.modules.module.Module.register_full_backward_pre_hook:8
msgid ""
"The :attr:`grad_output` is a tuple. The hook should not modify its "
"arguments, but it can optionally return a new gradient with respect to "
"the output that will be used in place of :attr:`grad_output` in "
"subsequent computations. Entries in :attr:`grad_output` will be ``None`` "
"for all non-Tensor arguments."
msgstr ""

#: of torch.nn.modules.module.Module.register_full_backward_pre_hook:19
msgid ""
"Modifying inputs inplace is not allowed when using backward hooks and "
"will raise an error."
msgstr ""

#: of torch.nn.modules.module.Module.register_full_backward_pre_hook:24
msgid ""
"If true, the provided ``hook`` will be fired before all existing "
"``backward_pre`` hooks on this :class:`torch.nn.modules.Module`. "
"Otherwise, the provided ``hook`` will be fired after all existing "
"``backward_pre`` hooks on this :class:`torch.nn.modules.Module`. Note "
"that global ``backward_pre`` hooks registered with "
":func:`register_module_full_backward_pre_hook` will fire before all hooks"
" registered by this method."
msgstr ""

#: of torch.nn.modules.module.Module.register_load_state_dict_post_hook:1
msgid ""
"Register a post hook to be run after module's ``load_state_dict`` is "
"called."
msgstr ""

#: of torch.nn.modules.module.Module.register_load_state_dict_post_hook:3
msgid "It should have the following signature::"
msgstr ""

#: of torch.nn.modules.module.Module.register_load_state_dict_post_hook:4
msgid "hook(module, incompatible_keys) -> None"
msgstr ""

#: of torch.nn.modules.module.Module.register_load_state_dict_post_hook:6
msgid ""
"The ``module`` argument is the current module that this hook is "
"registered on, and the ``incompatible_keys`` argument is a ``NamedTuple``"
" consisting of attributes ``missing_keys`` and ``unexpected_keys``. "
"``missing_keys`` is a ``list`` of ``str`` containing the missing keys and"
" ``unexpected_keys`` is a ``list`` of ``str`` containing the unexpected "
"keys."
msgstr ""

#: of torch.nn.modules.module.Module.register_load_state_dict_post_hook:12
msgid "The given incompatible_keys can be modified inplace if needed."
msgstr ""

#: of torch.nn.modules.module.Module.register_load_state_dict_post_hook:14
msgid ""
"Note that the checks performed when calling :func:`load_state_dict` with "
"``strict=True`` are affected by modifications the hook makes to "
"``missing_keys`` or ``unexpected_keys``, as expected. Additions to either"
" set of keys will result in an error being thrown when ``strict=True``, "
"and clearing out both missing and unexpected keys will avoid an error."
msgstr ""

#: of torch.nn.modules.module.Module.register_module:1
msgid "Alias for :func:`add_module`."
msgstr ""

#: of torch.nn.modules.module.Module.register_parameter:1
msgid "Add a parameter to the module."
msgstr ""

#: of torch.nn.modules.module.Module.register_parameter:3
msgid "The parameter can be accessed as an attribute using given name."
msgstr ""

#: of torch.nn.modules.module.Module.register_parameter:5
msgid ""
"name of the parameter. The parameter can be accessed from this module "
"using the given name"
msgstr ""

#: of torch.nn.modules.module.Module.register_parameter:8
msgid ""
"parameter to be added to the module. If ``None``, then operations that "
"run on parameters, such as :attr:`cuda`, are ignored. If ``None``, the "
"parameter is **not** included in the module's :attr:`state_dict`."
msgstr ""

#: of torch.nn.modules.module.Module.register_state_dict_pre_hook:1
msgid ""
"Register a pre-hook for the :meth:`~torch.nn.Module.load_state_dict` "
"method."
msgstr ""

#: of torch.nn.modules.module.Module.register_state_dict_pre_hook:3
msgid ""
"These hooks will be called with arguments: ``self``, ``prefix``, and "
"``keep_vars`` before calling ``state_dict`` on ``self``. The registered "
"hooks can be used to perform pre-processing before the ``state_dict`` "
"call is made."
msgstr ""

#: of torch.nn.modules.module.Module.requires_grad_:1
msgid "Change if autograd should record operations on parameters in this module."
msgstr ""

#: of torch.nn.modules.module.Module.requires_grad_:3
msgid ""
"This method sets the parameters' :attr:`requires_grad` attributes in-"
"place."
msgstr ""

#: of torch.nn.modules.module.Module.requires_grad_:6
msgid ""
"This method is helpful for freezing part of the module for finetuning or "
"training parts of a model individually (e.g., GAN training)."
msgstr ""

#: of torch.nn.modules.module.Module.requires_grad_:9
msgid ""
"See :ref:`locally-disable-grad-doc` for a comparison between "
"`.requires_grad_()` and several similar mechanisms that may be confused "
"with it."
msgstr ""

#: of torch.nn.modules.module.Module.requires_grad_:12
msgid ""
"whether autograd should record operations on parameters in this module. "
"Default: ``True``."
msgstr ""

#: of torch.nn.modules.module.Module.set_extra_state:1
msgid "Set extra state contained in the loaded `state_dict`."
msgstr ""

#: of torch.nn.modules.module.Module.set_extra_state:3
msgid ""
"This function is called from :func:`load_state_dict` to handle any extra "
"state found within the `state_dict`. Implement this function and a "
"corresponding :func:`get_extra_state` for your module if you need to "
"store extra state within its `state_dict`."
msgstr ""

#: of torch.nn.modules.module.Module.set_extra_state:8
msgid "Extra state from the `state_dict`"
msgstr ""

#: of torch.nn.modules.module.Module.share_memory:1
msgid "See :meth:`torch.Tensor.share_memory_`."
msgstr ""

#: of torch.nn.modules.module.Module.state_dict:1
msgid ""
"Return a dictionary containing references to the whole state of the "
"module."
msgstr ""

#: of torch.nn.modules.module.Module.state_dict:3
msgid ""
"Both parameters and persistent buffers (e.g. running averages) are "
"included. Keys are corresponding parameter and buffer names. Parameters "
"and buffers set to ``None`` are not included."
msgstr ""

#: of torch.nn.modules.module.Module.state_dict:8
msgid ""
"The returned object is a shallow copy. It contains references to the "
"module's parameters and buffers."
msgstr ""

#: of torch.nn.modules.module.Module.state_dict:12
msgid ""
"Currently ``state_dict()`` also accepts positional arguments for "
"``destination``, ``prefix`` and ``keep_vars`` in order. However, this is "
"being deprecated and keyword arguments will be enforced in future "
"releases."
msgstr ""

#: of torch.nn.modules.module.Module.state_dict:18
msgid ""
"Please avoid the use of argument ``destination`` as it is not designed "
"for end-users."
msgstr ""

#: of torch.nn.modules.module.Module.state_dict:21
msgid ""
"If provided, the state of module will be updated into the dict and the "
"same object is returned. Otherwise, an ``OrderedDict`` will be created "
"and returned. Default: ``None``."
msgstr ""

#: of torch.nn.modules.module.Module.state_dict:26
msgid ""
"a prefix added to parameter and buffer names to compose the keys in "
"state_dict. Default: ``''``."
msgstr ""

#: of torch.nn.modules.module.Module.state_dict:29
msgid ""
"by default the :class:`~torch.Tensor` s returned in the state dict are "
"detached from autograd. If it's set to ``True``, detaching will not be "
"performed. Default: ``False``."
msgstr ""

#: of torch.nn.modules.module.Module.state_dict:35
msgid "a dictionary containing a whole state of the module"
msgstr ""

#: of torch.nn.modules.module.Module.to:1
msgid "Move and/or cast the parameters and buffers."
msgstr ""

#: of torch.nn.modules.module.Module.to:3
msgid "This can be called as"
msgstr ""

#: of torch.nn.modules.module.Module.to:17
msgid ""
"Its signature is similar to :meth:`torch.Tensor.to`, but only accepts "
"floating point or complex :attr:`dtype`\\ s. In addition, this method "
"will only cast the floating point or complex parameters and buffers to "
":attr:`dtype` (if given). The integral parameters and buffers will be "
"moved :attr:`device`, if that is given, but with dtypes unchanged. When "
":attr:`non_blocking` is set, it tries to convert/move asynchronously with"
" respect to the host if possible, e.g., moving CPU Tensors with pinned "
"memory to CUDA devices."
msgstr ""

#: of torch.nn.modules.module.Module.to:26
msgid "See below for examples."
msgstr ""

#: of torch.nn.modules.module.Module.to:31
msgid "the desired device of the parameters and buffers in this module"
msgstr ""

#: of torch.nn.modules.module.Module.to:34
msgid ""
"the desired floating point or complex dtype of the parameters and buffers"
" in this module"
msgstr ""

#: of torch.nn.modules.module.Module.to:37
msgid ""
"Tensor whose dtype and device are the desired dtype and device for all "
"parameters and buffers in this module"
msgstr ""

#: of torch.nn.modules.module.Module.to:40
msgid ""
"the desired memory format for 4D parameters and buffers in this module "
"(keyword only argument)"
msgstr ""

#: of torch.nn.modules.module.Module.to:48
msgid "Examples::"
msgstr ""

#: of torch.nn.modules.module.Module.to_empty:1
msgid ""
"Move the parameters and buffers to the specified device without copying "
"storage."
msgstr ""

#: of torch.nn.modules.module.Module.to_empty:3
msgid "The desired device of the parameters and buffers in this module."
msgstr ""

#: of torch.nn.modules.module.Module.to_empty:6
msgid ""
"Whether parameters and buffers of submodules should be recursively moved "
"to the specified device."
msgstr ""

#: of torch.nn.modules.module.Module.train:1
msgid "Set the module in training mode."
msgstr ""

#: of torch.nn.modules.module.Module.train:8
msgid ""
"whether to set training mode (``True``) or evaluation mode (``False``). "
"Default: ``True``."
msgstr ""

#: of torch.nn.modules.module.Module.type:1
msgid "Casts all parameters and buffers to :attr:`dst_type`."
msgstr ""

#: of torch.nn.modules.module.Module.type:6
msgid "the desired type"
msgstr ""

#: of torch.nn.modules.module.Module.xpu:1
msgid "Move all model parameters and buffers to the XPU."
msgstr ""

#: of torch.nn.modules.module.Module.xpu:3
msgid ""
"This also makes associated parameters and buffers different objects. So "
"it should be called before constructing optimizer if the module will live"
" on XPU while being optimized."
msgstr ""

#: of torch.nn.modules.module.Module.zero_grad:1
msgid "Reset gradients of all model parameters."
msgstr ""

#: of torch.nn.modules.module.Module.zero_grad:3
msgid ""
"See similar function under :class:`torch.optim.Optimizer` for more "
"context."
msgstr ""

#: of torch.nn.modules.module.Module.zero_grad:5
msgid ""
"instead of setting to zero, set the grads to None. See "
":meth:`torch.optim.Optimizer.zero_grad` for details."
msgstr ""

#: ../../source/api/translation.rst:2
msgid "tensorcircuit.translation"
msgstr ""

#: of tensorcircuit.translation:1
msgid "Circuit object translation in different packages"
msgstr ""

#: of tensorcircuit.translation.cirq2tc:1
msgid "Generate a tensorcircuit circuit from the cirq circuit."
msgstr ""

#: of tensorcircuit.translation.cirq2tc:3
msgid "A quantum circuit in cirq"
msgstr ""

#: of tensorcircuit.translation.cirq2tc:5 tensorcircuit.translation.stim2tc:5
msgid "# of qubits, defaults to None"
msgstr ""

#: of tensorcircuit.translation.cirq2tc:7
msgid "Input state of the circuit, defaults to None"
msgstr ""

#: of tensorcircuit.translation.cirq2tc:9 tensorcircuit.translation.stim2tc:7
msgid "whether to use DMCircuit, defaults to False"
msgstr ""

#: of tensorcircuit.translation.eqasm2tc:1
msgid "Translation qexe/eqasm instruction to tensorcircuit Circuit object"
msgstr ""

#: of tensorcircuit.translation.eqasm2tc:7
msgid "lines of ignored code at the head and the tail, defaults to (6, 1)"
msgstr ""

#: of tensorcircuit.translation.perm_matrix:1
msgid ""
"Generate a permutation matrix P. Due to the different convention or "
"qubits' order in qiskit and tensorcircuit, the unitary represented by the"
" same circuit is different. They are related by this permutation matrix "
"P: P @ U_qiskit @ P = U_tc"
msgstr ""

#: of tensorcircuit.translation.perm_matrix:7
#: tensorcircuit.translation.qir2cirq:15
#: tensorcircuit.translation.qir2qiskit:16
#: tensorcircuit.translation.qiskit2tc:13 tensorcircuit.vis.qir2tex:12
msgid "# of qubits"
msgstr ""

#: of tensorcircuit.translation.perm_matrix:9
msgid "The permutation matrix P"
msgstr ""

#: of tensorcircuit.translation.qir2cirq:1
msgid ""
"Generate a cirq circuit using the quantum intermediate representation "
"(qir) in tensorcircuit."
msgstr ""

#: of tensorcircuit.translation.qir2cirq:17
#: tensorcircuit.translation.qir2qiskit:18
msgid ""
"The extra quantum IR of tc circuit including measure and reset on "
"hardware, defaults to None"
msgstr ""

#: of tensorcircuit.translation.qir2cirq:20
msgid "qiskit cirq object"
msgstr ""

#: of tensorcircuit.translation.qir2cirq:23
msgid ""
"#TODO(@erertertet): add default theta to iswap gate add more cirq built-"
"in gate instead of customized add unitary test with tolerance add support"
" of cirq built-in ControlledGate for multiplecontroll support more "
"element in qir, e.g. barrier, measure..."
msgstr ""

#: of tensorcircuit.translation.qir2json:1
msgid ""
"transform qir to json compatible list of dict where array is replaced by "
"real and imaginary list"
msgstr ""

#: of tensorcircuit.translation.qir2qiskit:1
msgid ""
"Generate a qiskit quantum circuit using the quantum intermediate "
"representation (qir) in tensorcircuit."
msgstr ""

#: of tensorcircuit.translation.qir2qiskit:21
msgid "Circuit initial state in qiskit format"
msgstr ""

#: of tensorcircuit.translation.qir2qiskit:23
msgid "qiskit QuantumCircuit object"
msgstr ""

#: of tensorcircuit.translation.qiskit2tc:1
msgid "Generate a tensorcircuit circuit from the qiskit circuit."
msgstr ""

#: of tensorcircuit.translation.qiskit2tc:11
msgid "A quantum circuit in qiskit."
msgstr ""

#: of tensorcircuit.translation.qiskit2tc:15
msgid "Input state of the circuit. Default is None."
msgstr ""

#: of tensorcircuit.translation.qiskit2tc:17
msgid "``Circuit``, ``DMCircuit`` or ``MPSCircuit``"
msgstr ""

#: of tensorcircuit.translation.qiskit2tc:21
msgid ""
"(variational) parameters for the circuit. Could be either a sequence or "
"dictionary depending on the type of parameters in the Qiskit circuit. For"
" ``ParameterVectorElement`` use sequence. For ``Parameter`` use "
"dictionary."
msgstr ""

#: of tensorcircuit.translation.qiskit2tc:25
msgid "A quantum circuit in tensorcircuit"
msgstr ""

#: of tensorcircuit.translation.qiskit_from_qasm_str_ordered_measure:1
msgid ""
"qiskit ``from_qasm_str`` method cannot keep the order of measure as the "
"qasm file, we provide this alternative function in case the order of "
"measure instruction matters"
msgstr ""

#: of tensorcircuit.translation.qiskit_from_qasm_str_ordered_measure:4
msgid "open qasm str"
msgstr ""

#: of tensorcircuit.translation.qiskit_from_qasm_str_ordered_measure:6
msgid "``qiskit.circuit.QuantumCircuit``"
msgstr ""

#: of tensorcircuit.translation.stim2tc:1
msgid "Generate a tensorcircuit circuit from the stim circuit."
msgstr ""

#: of tensorcircuit.translation.stim2tc:3
msgid "A quantum circuit in stim"
msgstr ""

#: of tensorcircuit.translation.stim2tc:13
msgid "_description|"
msgstr ""

#: ../../source/api/u1circuit.rst:2
msgid "tensorcircuit.u1circuit"
msgstr ""

#: of tensorcircuit.u1circuit:1
msgid "Circuit class for U(1) conserving circuits."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit:1
msgid ""
"Circuit class for U(1) conserving circuits (particle number "
"conservation). The simulation is performed entirely within the compressed"
" U(1) subspace, enabling efficient simulation of systems where the total "
"number of excitations is preserved."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit:6
msgid ""
"Note: Supports up to 64 qubits by utilizing 64-bit integer bitmasks for "
"basis state representation."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.__init__:1
msgid "Initialize a U(1) conserving circuit."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.__init__:3
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.__init__:3
msgid "Number of qubits in the circuit."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.__init__:5
msgid ""
"Total number of excitations (particles) to preserve. If None, it is "
"inferred from the length of ``filled``."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.__init__:8
msgid ""
"Initial indices of qubits that are occupied (|1> state). If provided and "
"``inputs`` is None, the initial state is set to this computational basis "
"state."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.__init__:12
msgid ""
"Initial state vector already defined in the U(1) subspace. If provided, "
"must have shape (comb(nqubits, k),)."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.apply_general_gate:1
msgid "Apply a gate by name. Called by _meta_apply generated methods."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.apply_general_gate:3
msgid "Gate tensor (ignored, dispatch is by name)"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.apply_general_gate:4
msgid "Qubit indices"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.apply_general_gate:5
msgid "Gate name (rz, rzz, cz, cphase, swap, iswap)"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.apply_general_gate:6
msgid "Split configuration (ignored in U1Circuit)"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.apply_general_gate:7
msgid "MPO flag (ignored in U1Circuit)"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.apply_general_gate:8
msgid "Diagonal flag (ignored in U1Circuit)"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.apply_general_gate:9
msgid "QIR dictionary for recording"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.apply_general_gate:10
msgid "Extra parameters"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.entanglement_entropy:1
msgid "Compute the von Neumann entanglement entropy for the specified qubits."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.entanglement_entropy:3
msgid "The qubits to keep."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.entanglement_entropy:5
#: tensorcircuit.u1circuit.U1Circuit.reduced_density_matrix:5
msgid "The qubits to trace out."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.entanglement_entropy:7
msgid "Entanglement entropy."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.expectation:1
msgid "Compute expectation value of operators."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.expectation:3
msgid ""
"Currently only supports single Z operator via expectation_z. For general "
"operators, please use expectation_ps."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.expectation:6
msgid "Operator specification"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.expectation_ps:1
msgid "Compute expectation value of a Pauli string."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.expectation_ps:3
msgid "Qubit indices for X operators"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.expectation_ps:4
msgid "Qubit indices for Y operators"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.expectation_ps:5
msgid "Qubit indices for Z operators"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.expectation_ps:6
msgid "Alternative Pauli string specification (0=I, 1=X, 2=Y, 3=Z)"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.expectation_ps:7
msgid "Expectation value <P>"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.expectation_z:1
msgid "Compute expectation value of Z operator on qubit i."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.expectation_z:3
msgid "Qubit index"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.expectation_z:4
msgid "Expectation value <Z_i>"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.measure:1
msgid "Measure specific qubits in the computational basis."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.measure:3
msgid ""
"This is a simplified measurement that samples from the full state and "
"returns the values at the specified qubit indices."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.measure:6
msgid "Qubit indices to measure"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.measure:8
msgid "If true, return the probability of the outcome"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.measure:10
msgid "External randomness tensor, shape [1]"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.measure:12
msgid "Tuple of (measurement outcomes tensor, probability or -1.0)"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.probability:1
msgid "Get the probability vector over the U(1) conserved basis states."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.probability:3
msgid "Probability vector of shape [dim] where dim = C(n, k)"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.probability_full:1
msgid "Get the probability vector over the full 2^n computational basis."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.probability_full:3
msgid "Probability vector of shape [2^nqubits]"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.reduced_density_matrix:7
msgid "If true, return a list of RDM blocks for each k_A."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.reduced_density_matrix:9
msgid "The reduced density matrix or a list of blocks."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.sample:1
msgid "Sample from the U(1) circuit state."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.sample:3
msgid "Number of samples, defaults to None (single sample)"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.sample:5
msgid "If true, sample from the state vector directly"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.sample:7
#, python-brace-format
msgid ""
"Sample format. Options: - None: returns list of (binary_config_tensor, "
"probability) tuples - \"sample_int\": np.array of integer samples - "
"\"sample_bin\": list of binary arrays - \"count_vector\": count vector "
"over full basis - \"count_tuple\": (unique_values, counts) - "
"\"count_dict_bin\": {\"01..\": count, ...} - \"count_dict_int\": {int: "
"count, ...}"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.sample:18
msgid "Random generator, defaults to None"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.sample:20
msgid "External randomness tensor uniformly from [0, 1], shape [batch]"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.sample:22
msgid "Keep full size for jit compatibility, defaults to True"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.sample:24
msgid "Samples in the specified format"
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.state:1
#: tensorcircuit.u1circuit.U1Circuit.wavefunction:1
msgid "Return the state vector in the U(1) conserved subspace."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.to_dense:1
msgid "Convert the U(1) conserved state to the full 2^n Hilbert space."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.to_dense:3
msgid ""
"Returns a state vector of shape [2^nqubits] where non-zero amplitudes "
"appear only at positions corresponding to basis states with exactly k "
"filled qubits."
msgstr ""

#: of tensorcircuit.u1circuit.U1Circuit.to_dense:7
msgid "State vector in full Hilbert space"
msgstr ""

#: ../../source/api/utils.rst:2
msgid "tensorcircuit.utils"
msgstr ""

#: of tensorcircuit.utils:1
msgid "Helper functions"
msgstr ""

#: of tensorcircuit.utils.append:1
msgid "Functional programming paradigm to build function pipeline"
msgstr ""

#: of tensorcircuit.utils.append:9
msgid "The function which are attached with other functions"
msgstr ""

#: of tensorcircuit.utils.append:11
msgid "Function to be attached"
msgstr ""

#: of tensorcircuit.utils.append:13
msgid "The final results after function pipeline"
msgstr ""

#: of tensorcircuit.utils.arg_alias:1
msgid "function argument alias decorator with new docstring"
msgstr ""

#: of tensorcircuit.utils.arg_alias:7
msgid "whether to add doc for these new alias arguments, defaults True"
msgstr ""

#: of tensorcircuit.utils.arg_alias:9
msgid "the decorated function"
msgstr ""

#: of tensorcircuit.utils.benchmark:1
msgid "benchmark jittable function with staging time and running time"
msgstr ""

#: of tensorcircuit.utils.benchmark:5
msgid "_description_, defaults to 5"
msgstr ""

#: of tensorcircuit.utils.gpu_memory_share:1
msgid "Set the GPU memory growth mode"
msgstr ""

#: of tensorcircuit.utils.gpu_memory_share:3
msgid "whether to set the GPU memory growth mode, defaults to True"
msgstr ""

#: of tensorcircuit.utils.gpu_memory_share:5
msgid "None"
msgstr ""

#: of tensorcircuit.utils.is_m1mac:1
msgid "check whether the running platform is MAC with M1 chip"
msgstr ""

#: of tensorcircuit.utils.is_m1mac:3
msgid "True for MAC M1 platform"
msgstr ""

#: of tensorcircuit.utils.return_partial:1
msgid ""
"Return a callable function for output ith parts of the original output "
"along the first axis. Original output supports List and Tensor."
msgstr ""

#: of tensorcircuit.utils.return_partial:20
msgid "The function to be applied this method"
msgstr ""

#: of tensorcircuit.utils.return_partial:22
msgid "The ith parts of original output along the first axis (axis=0 or dim=0)"
msgstr ""

#: of tensorcircuit.utils.return_partial:24
msgid "The modified callable function"
msgstr ""

#: ../../source/api/vis.rst:2
msgid "tensorcircuit.vis"
msgstr ""

#: of tensorcircuit.vis:1
msgid "Visualization on circuits"
msgstr ""

#: of tensorcircuit.vis.gate_name_trans:1
msgid ""
"Translating from the gate name to gate information including the number "
"of control qubits and the reduced gate name."
msgstr ""

#: of tensorcircuit.vis.gate_name_trans:10
msgid "String of gate name"
msgstr ""

#: of tensorcircuit.vis.gate_name_trans:12
msgid "# of control qubits, reduced gate name"
msgstr ""

#: of tensorcircuit.vis.qir2tex:1
msgid ""
"Generate Tex code from 'qir' string to illustrate the circuit structure. "
"This visualization is based on quantikz package."
msgstr ""

#: of tensorcircuit.vis.qir2tex:10
msgid "The quantum intermediate representation of a circuit in tensorcircuit."
msgstr ""

#: of tensorcircuit.vis.qir2tex:14
msgid "Initial state, default is an all zero state '000...000'."
msgstr ""

#: of tensorcircuit.vis.qir2tex:16
msgid ""
"Measurement Basis, default is None which means no measurement in the end "
"of the circuit."
msgstr ""

#: of tensorcircuit.vis.qir2tex:19
msgid ""
"If true, a right compression of the circuit will be conducted. A right "
"compression means we will try to shift gates from right to left if "
"possible."
msgstr ""

#: of tensorcircuit.vis.qir2tex:21
msgid ""
"Default is false. :type rcompress: bool :param lcompress: If true, a left"
" compression of the circuit will be conducted."
msgstr ""

#: of tensorcircuit.vis.qir2tex:24
msgid ""
"A left compression means we will try to shift gates from left to right if"
" possible. Default is false."
msgstr ""

#: of tensorcircuit.vis.qir2tex:27
msgid ""
"If true, the tex code will be designed to generate a standalone document."
" Default is false which means the generated tex code is just a quantikz "
"code block."
msgstr ""

#: of tensorcircuit.vis.qir2tex:30
msgid ""
"If true, a string table of tex code will also be returned. Default is "
"false."
msgstr ""

#: of tensorcircuit.vis.qir2tex:33
msgid ""
"Tex code of circuit visualization based on quantikz package. If "
"return_string_table is true, a string table of tex code will also be "
"returned."
msgstr ""

#: of tensorcircuit.vis.render_pdf:1
msgid ""
"Generate the PDF file with given latex string and filename. Latex command"
" and file path can be specified. When notebook is True, convert the "
"output PDF file to image and return a Image object."
msgstr ""

#: of tensorcircuit.vis.render_pdf:15
msgid "String of latex content"
msgstr ""

#: of tensorcircuit.vis.render_pdf:17
msgid "File name, defaults to random UUID `str(uuid4())`"
msgstr ""

#: of tensorcircuit.vis.render_pdf:19
msgid "Executable Latex command, defaults to `pdflatex`"
msgstr ""

#: of tensorcircuit.vis.render_pdf:21
msgid "File path, defaults to current working place `os.getcwd()`"
msgstr ""

#: of tensorcircuit.vis.render_pdf:25
msgid "if notebook is True, return `Image` object; otherwise return `None`"
msgstr ""

#: ../../source/api/zx.rst:2
msgid "tensorcircuit.zx"
msgstr ""

#: ../../source/api/zx/converter.rst:2
msgid "tensorcircuit.zx.converter"
msgstr ""

#: of tensorcircuit.zx.converter:1
msgid ""
"Converter from TensorCircuit to PyZX. Includes graph preparation and "
"reduction utilities."
msgstr ""

#: of tensorcircuit.zx.converter.GraphRepresentation.add_vertex:1
msgid "Add a vertex to the graph."
msgstr ""

#: of tensorcircuit.zx.converter.GraphRepresentation.add_vertex:3
msgid "Vertex type, defaults to VertexType.Z."
msgstr ""

#: of tensorcircuit.zx.converter.GraphRepresentation.add_vertex:5
msgid "Qubit index, defaults to -1."
msgstr ""

#: of tensorcircuit.zx.converter.GraphRepresentation.add_vertex:7
msgid "Row index for layout, defaults to -1."
msgstr ""

#: of tensorcircuit.zx.converter.GraphRepresentation.add_vertex:9
msgid "Vertex phase, defaults to 0."
msgstr ""

#: of tensorcircuit.zx.converter.GraphRepresentation.add_vertex:11
#: tensorcircuit.zx.converter.add_dummy:9
msgid "The index of the new vertex."
msgstr ""

#: of tensorcircuit.zx.converter.GraphRepresentation.edge_st:1
msgid "Get the endpoints of an edge."
msgstr ""

#: of tensorcircuit.zx.converter.GraphRepresentation.edge_st:3
msgid "Edge."
msgstr ""

#: of tensorcircuit.zx.converter.GraphRepresentation.edge_st:5
msgid "Tuple of vertex indices (v1, v2)."
msgstr ""

#: of tensorcircuit.zx.converter.GraphRepresentation.incident_edges:1
msgid "Get edges incident to a vertex."
msgstr ""

#: of tensorcircuit.zx.converter.GraphRepresentation.incident_edges:3
msgid "Vertex index."
msgstr ""

#: of tensorcircuit.zx.converter.GraphRepresentation.incident_edges:5
msgid "Iterable of edges."
msgstr ""

#: of tensorcircuit.zx.converter.GraphRepresentation.observables:1
msgid "List of observable vertices."
msgstr ""

#: of tensorcircuit.zx.converter.GraphRepresentation.observables:3
msgid "List of vertex indices."
msgstr ""

#: of tensorcircuit.zx.converter.add_dummy:1
msgid "Add a dummy boundary vertex to a qubit lane."
msgstr ""

#: of tensorcircuit.zx.converter.add_dummy:3
#: tensorcircuit.zx.converter.add_lane:3
#: tensorcircuit.zx.converter.depolarize1:3
#: tensorcircuit.zx.converter.depolarize2:3
#: tensorcircuit.zx.converter.detector:3
#: tensorcircuit.zx.converter.ensure_lane:3
#: tensorcircuit.zx.converter.last_edge:3 tensorcircuit.zx.converter.last_row:3
#: tensorcircuit.zx.converter.mx:3 tensorcircuit.zx.converter.my:3
#: tensorcircuit.zx.converter.observable_include:3
#: tensorcircuit.zx.converter.pauli_channel_1:3
#: tensorcircuit.zx.converter.pauli_channel_2:3
#: tensorcircuit.zx.converter.reset_x:3 tensorcircuit.zx.converter.reset_y:3
#: tensorcircuit.zx.converter.reset_z:3 tensorcircuit.zx.converter.x_phase:3
#: tensorcircuit.zx.converter.z_phase:3
msgid "The graph representation."
msgstr ""

#: of tensorcircuit.zx.converter.add_dummy:5
#: tensorcircuit.zx.converter.add_lane:5
#: tensorcircuit.zx.converter.ensure_lane:5
#: tensorcircuit.zx.converter.last_edge:5 tensorcircuit.zx.converter.last_row:5
#: tensorcircuit.zx.converter.x_phase:5 tensorcircuit.zx.converter.z_phase:5
msgid "The qubit index."
msgstr ""

#: of tensorcircuit.zx.converter.add_dummy:7
msgid "Row index, defaults to last_row + 1."
msgstr ""

#: of tensorcircuit.zx.converter.add_lane:1
msgid "Initialize a new qubit lane with two boundary vertices."
msgstr ""

#: of tensorcircuit.zx.converter.add_lane:7
msgid "The index of the first vertex."
msgstr ""

#: of tensorcircuit.zx.converter.build_amplitude_graph:1
msgid "Build a scalar graph representing the amplitude <bitstring|psi>."
msgstr ""

#: of tensorcircuit.zx.converter.build_amplitude_graph:3
#: tensorcircuit.zx.converter.build_sampling_graph:3
msgid "The input ZX representation."
msgstr ""

#: of tensorcircuit.zx.converter.build_amplitude_graph:5
msgid "The target bitstring as a sequence of 0s and 1s."
msgstr ""

#: of tensorcircuit.zx.converter.build_amplitude_graph:7
msgid "The scalar ZX graph."
msgstr ""

#: of tensorcircuit.zx.converter.build_sampling_graph:1
msgid "Build a doubled sampling graph from a ZX representation."
msgstr ""

#: of tensorcircuit.zx.converter.build_sampling_graph:5
msgid ""
"Whether to prepare for detector/observable sampling or measurement "
"records."
msgstr ""

#: of tensorcircuit.zx.converter.build_sampling_graph:7
msgid ""
"Optional Pauli string to insert at the junction for expectation values. "
"Dictionary mapping qubit index to Pauli operator ('I', 'X', 'Y', 'Z')."
msgstr ""

#: of tensorcircuit.zx.converter.build_sampling_graph:10
msgid "The prepared ZX graph."
msgstr ""

#: of tensorcircuit.zx.converter.circuit_to_zx:1
msgid ""
"Convert a TensorCircuit AbstractCircuit to a ZX-calculus "
"GraphRepresentation."
msgstr ""

#: of tensorcircuit.zx.converter.circuit_to_zx:3
msgid "The source circuit."
msgstr ""

#: of tensorcircuit.zx.converter.circuit_to_zx:5
msgid "Whether to force measurements on all qubits at the end, defaults to False."
msgstr ""

#: of tensorcircuit.zx.converter.circuit_to_zx:7
msgid "The ZX graph representation of the circuit."
msgstr ""

#: of tensorcircuit.zx.converter.depolarize1:1
msgid "Apply single-qubit depolarizing channel."
msgstr ""

#: of tensorcircuit.zx.converter.depolarize1:5 tensorcircuit.zx.converter.mx:5
#: tensorcircuit.zx.converter.my:5 tensorcircuit.zx.converter.pauli_channel_1:5
#: tensorcircuit.zx.converter.reset_x:5 tensorcircuit.zx.converter.reset_y:5
#: tensorcircuit.zx.converter.reset_z:5
msgid "Qubit index."
msgstr ""

#: of tensorcircuit.zx.converter.depolarize1:7
#: tensorcircuit.zx.converter.depolarize2:9
msgid "Depolarizing probability."
msgstr ""

#: of tensorcircuit.zx.converter.depolarize2:1
msgid "Apply two-qubit depolarizing channel."
msgstr ""

#: of tensorcircuit.zx.converter.depolarize2:5
#: tensorcircuit.zx.converter.pauli_channel_2:5
msgid "First qubit index."
msgstr ""

#: of tensorcircuit.zx.converter.depolarize2:7
#: tensorcircuit.zx.converter.pauli_channel_2:7
msgid "Second qubit index."
msgstr ""

#: of tensorcircuit.zx.converter.detector:1
msgid "Add a detector vertex defined by a set of measurement outcomes."
msgstr ""

#: of tensorcircuit.zx.converter.detector:5
msgid "Indices of measurement records involved in the detector."
msgstr ""

#: of tensorcircuit.zx.converter.ensure_lane:1
msgid "Ensure a qubit lane exists in the graph."
msgstr ""

#: of tensorcircuit.zx.converter.h_gate:1
msgid "Apply Hadamard gate."
msgstr ""

#: of tensorcircuit.zx.converter.h_xy:1
msgid ""
"Apply variant of Hadamard gate that swaps the X and Y axes (instead of X "
"and Z)."
msgstr ""

#: of tensorcircuit.zx.converter.h_yz:1
msgid ""
"Apply variant of Hadamard gate that swaps the Y and Z axes (instead of X "
"and Z)."
msgstr ""

#: of tensorcircuit.zx.converter.is_pauli:1
msgid "Check if a matrix is a Pauli matrix."
msgstr ""

#: of tensorcircuit.zx.converter.is_pauli:3
msgid "The matrix to check."
msgstr ""

#: of tensorcircuit.zx.converter.is_pauli:5
msgid "The name of the Pauli matrix ('i', 'x', 'y', 'z') or None."
msgstr ""

#: of tensorcircuit.zx.converter.last_edge:1
msgid "Get the last edge on a qubit lane."
msgstr ""

#: of tensorcircuit.zx.converter.last_edge:7
msgid "The last edge index."
msgstr ""

#: of tensorcircuit.zx.converter.last_row:1
msgid "Get the row index of the last vertex on a qubit lane."
msgstr ""

#: of tensorcircuit.zx.converter.last_row:7
msgid "Highest row index on the lane."
msgstr ""

#: of tensorcircuit.zx.converter.m:1
msgid "Measure qubit in Z basis with optional bit-flip error probability p."
msgstr ""

#: of tensorcircuit.zx.converter.mpp:1
msgid "Measure a multi-Pauli product."
msgstr ""

#: of tensorcircuit.zx.converter.mpp:3
msgid "Identical implementation to tsim.core.instructions.mpp."
msgstr ""

#: of tensorcircuit.zx.converter.mpp:5
msgid "The graph representation to modify."
msgstr ""

#: of tensorcircuit.zx.converter.mpp:6
msgid ""
"List of (pauli_type, qubit) pairs defining the Pauli product. pauli_type "
"should be 'X', 'Y', or 'Z'."
msgstr ""

#: of tensorcircuit.zx.converter.mpp:8
msgid "Whether to invert the measurement result."
msgstr ""

#: of tensorcircuit.zx.converter.mr:1 tensorcircuit.zx.converter.mrz:1
msgid "Z-basis demolition measurement (optionally noisy)."
msgstr ""

#: of tensorcircuit.zx.converter.mrx:1
msgid "X-basis demolition measurement (optionally noisy)."
msgstr ""

#: of tensorcircuit.zx.converter.mry:1
msgid "Y-basis demolition measurement (optionally noisy)."
msgstr ""

#: of tensorcircuit.zx.converter.mx:1
msgid "Apply X-basis measurement."
msgstr ""

#: of tensorcircuit.zx.converter.mx:7 tensorcircuit.zx.converter.my:7
#: tensorcircuit.zx.converter.reset_z:7
msgid "Error probability, defaults to 0."
msgstr ""

#: of tensorcircuit.zx.converter.mx:9 tensorcircuit.zx.converter.my:9
msgid "Whether to invert measurement result, defaults to False."
msgstr ""

#: of tensorcircuit.zx.converter.my:1
msgid "Apply Y-basis measurement."
msgstr ""

#: of tensorcircuit.zx.converter.observable_include:1
msgid "Add an observable vertex defined by a set of measurement outcomes."
msgstr ""

#: of tensorcircuit.zx.converter.observable_include:5
msgid "Indices of measurement records involved in the observable."
msgstr ""

#: of tensorcircuit.zx.converter.observable_include:7
msgid "Index of the observable."
msgstr ""

#: of tensorcircuit.zx.converter.pauli_channel_1:1
msgid "Apply single-qubit Pauli channel."
msgstr ""

#: of tensorcircuit.zx.converter.pauli_channel_1:7
msgid "Probability of X error, defaults to 0."
msgstr ""

#: of tensorcircuit.zx.converter.pauli_channel_1:9
msgid "Probability of Y error, defaults to 0."
msgstr ""

#: of tensorcircuit.zx.converter.pauli_channel_1:11
msgid "Probability of Z error, defaults to 0."
msgstr ""

#: of tensorcircuit.zx.converter.pauli_channel_2:1
msgid "Apply two-qubit Pauli channel."
msgstr ""

#: of tensorcircuit.zx.converter.pauli_channel_2:9
msgid "Probability of error IX."
msgstr ""

#: of tensorcircuit.zx.converter.pauli_channel_2:10
msgid "..."
msgstr ""

#: of tensorcircuit.zx.converter.prepare_graph:1
msgid "Prepare a circuit for sampling by converting to ZX and reducing."
msgstr ""

#: of tensorcircuit.zx.converter.prepare_graph:3
msgid "The input circuit."
msgstr ""

#: of tensorcircuit.zx.converter.prepare_graph:5
msgid "Whether to prepare for detector/observable sampling."
msgstr ""

#: of tensorcircuit.zx.converter.prepare_graph:7
msgid "Whether to force final measurements, defaults to False."
msgstr ""

#: of tensorcircuit.zx.converter.prepare_graph:9
msgid "Optional Pauli string to insert at the junction."
msgstr ""

#: of tensorcircuit.zx.converter.prepare_graph:11
msgid "A SamplingGraph object containing the reduced graph and metadata."
msgstr ""

#: of tensorcircuit.zx.converter.r_x:1
msgid "Apply R_X rotation gate with given phase (in units of π)."
msgstr ""

#: of tensorcircuit.zx.converter.r_y:1
msgid "Apply R_Y rotation gate with given phase (in units of π)."
msgstr ""

#: of tensorcircuit.zx.converter.r_z:1
msgid "Apply R_Z rotation gate with given phase (in units of π)."
msgstr ""

#: of tensorcircuit.zx.converter.reset_x:1
msgid "Reset qubit in X basis."
msgstr ""

#: of tensorcircuit.zx.converter.reset_y:1
msgid "Reset qubit in Y basis."
msgstr ""

#: of tensorcircuit.zx.converter.reset_z:1
msgid "Reset qubit to |0> state (Z-basis reset)."
msgstr ""

#: of tensorcircuit.zx.converter.sqrt_x:1
msgid "Apply SQRT_X gate."
msgstr ""

#: of tensorcircuit.zx.converter.sqrt_x_dag:1
msgid "Apply SQRT_X_DAG gate."
msgstr ""

#: of tensorcircuit.zx.converter.sqrt_y:1
msgid "Apply SQRT_Y gate."
msgstr ""

#: of tensorcircuit.zx.converter.sqrt_y_dag:1
msgid "Apply SQRT_Y_DAG gate."
msgstr ""

#: of tensorcircuit.zx.converter.sqrt_z:1
msgid "Apply SQRT_Z gate."
msgstr ""

#: of tensorcircuit.zx.converter.sqrt_z_dag:1
msgid "Apply SQRT_Z_DAG gate."
msgstr ""

#: of tensorcircuit.zx.converter.squash_graph:1
msgid "Compact the graph layout by re-assigning qubit and row indices."
msgstr ""

#: of tensorcircuit.zx.converter.squash_graph:3
#: tensorcircuit.zx.simplifier.full_reduce:3
#: tensorcircuit.zx.simplifier.t_count:3
#: tensorcircuit.zx.simplifier.teleport_reduce:3
msgid "The ZX graph."
msgstr ""

#: of tensorcircuit.zx.converter.transform_error_basis:1
msgid ""
"Transform error bit variables from original 'e' basis to a reduced 'f' "
"basis."
msgstr ""

#: of tensorcircuit.zx.converter.transform_error_basis:3
msgid "The ZX graph containing error variables."
msgstr ""

#: of tensorcircuit.zx.converter.transform_error_basis:5
msgid "Total number of error bits, defaults to None."
msgstr ""

#: of tensorcircuit.zx.converter.transform_error_basis:7
msgid "A tuple of (transformed_graph, basis_transformation_matrix)."
msgstr ""

#: of tensorcircuit.zx.converter.u3:1
msgid "Apply U3 gate: U3(θ,φ,λ) = R_Z(φ)·R_Y(θ)·R_Z(λ)."
msgstr ""

#: of tensorcircuit.zx.converter.x_gate:1
msgid "Apply Pauli X gate."
msgstr ""

#: of tensorcircuit.zx.converter.x_phase:1
msgid "Apply an X spider with the given phase to a qubit."
msgstr ""

#: of tensorcircuit.zx.converter.x_phase:7 tensorcircuit.zx.converter.z_phase:7
msgid "Phase in units of π."
msgstr ""

#: of tensorcircuit.zx.converter.y_error:1
msgid "Apply Y error with probability p."
msgstr ""

#: of tensorcircuit.zx.converter.y_gate:1
msgid "Apply Pauli Y gate."
msgstr ""

#: of tensorcircuit.zx.converter.y_phase:1
msgid "Apply Y-basis spider with the given phase."
msgstr ""

#: of tensorcircuit.zx.converter.z_gate:1
msgid "Apply Pauli Z gate."
msgstr ""

#: of tensorcircuit.zx.converter.z_phase:1
msgid "Apply a Z spider with the given phase to a qubit."
msgstr ""

#: ../../source/api/zx/evaluator.rst:2
msgid "tensorcircuit.zx.evaluator"
msgstr ""

#: of tensorcircuit.zx.evaluator:1
msgid "Evaluation of compiled scalar graphs using exact arithmetic."
msgstr ""

#: of tensorcircuit.zx.evaluator.ExactScalarArray:1
msgid ""
"Exact scalar array for ZX-calculus phase arithmetic using dyadic "
"representation."
msgstr ""

#: of tensorcircuit.zx.evaluator.ExactScalarArray:3
msgid ""
"Represents values of the form (c_0 + c_1·ω + c_2·ω² + c_3·ω³) × 2^power "
"where ω = e^(iπ/4)."
msgstr ""

#: of tensorcircuit.zx.evaluator.ExactScalarArray.coeffs:1
#: tensorcircuit.zx.scalar_graph.CompiledComponent.output_indices:1
#: tensorcircuit.zx.scalar_graph.CompiledProgram.components:1
#: tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.num_graphs:1
msgid "Alias for field number 0"
msgstr ""

#: of tensorcircuit.zx.evaluator.ExactScalarArray.power:1
#: tensorcircuit.zx.scalar_graph.CompiledComponent.f_selection:1
#: tensorcircuit.zx.scalar_graph.CompiledProgram.num_f_params:1
#: tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.n_params:1
msgid "Alias for field number 1"
msgstr ""

#: of tensorcircuit.zx.evaluator.ExactScalarArray.to_complex:1
msgid "Convert the exact scalar to a complex JAX array."
msgstr ""

#: of tensorcircuit.zx.evaluator.ExactScalarArray.to_complex:3
msgid "Complex representation of the scalar."
msgstr ""

#: of tensorcircuit.zx.evaluator.evaluate:1
msgid "Evaluate a compiled scalar graph circuit with batched parameter values."
msgstr ""

#: of tensorcircuit.zx.evaluator.evaluate:3
msgid "The compiled scalar graph program."
msgstr ""

#: of tensorcircuit.zx.evaluator.evaluate:5
msgid "Array of parameter bit values (f-basis and measurement records)."
msgstr ""

#: of tensorcircuit.zx.evaluator.evaluate:7
msgid "Evaluation results as a complex JAX array."
msgstr ""

#: ../../source/api/zx/noise_model.rst:2
msgid "tensorcircuit.zx.noise_model"
msgstr ""

#: of tensorcircuit.zx.noise_model:1
msgid "Pauli noise channels and error sampling infrastructure."
msgstr ""

#: of tensorcircuit.zx.noise_model.Channel:1
msgid "A probability distribution over error outcomes."
msgstr ""

#: of tensorcircuit.zx.noise_model.Channel:5
msgid "Shape (2^k,) probability array, sums to 1, dtype float64"
msgstr ""

#: of tensorcircuit.zx.noise_model.Channel:7
msgid "Any"
msgstr ""

#: of tensorcircuit.zx.noise_model.Channel:11
msgid "Tuple of column IDs, where each ID corresponds to a bit of the channel."
msgstr ""

#: of tensorcircuit.zx.noise_model.Channel:13
msgid "tuple[int, ...]"
msgstr ""

#: of tensorcircuit.zx.noise_model.Channel.num_bits:1
msgid "Number of bits in the channel (k where probs has shape 2^k)."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler:1
msgid "Samples from multiple error channels and transforms to a reduced basis."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler:3
msgid ""
"This class combines multiple error channels (each producing error bits "
"e0, e1, ...) and applies a linear transformation over GF(2) to convert "
"samples from the original \"e\" basis to a reduced \"f\" basis using "
"geometric-skip sampling optimized for low-noise regimes."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler:8
msgid "f_i = error_transform_ij * e_j mod 2"
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler:10
msgid ""
"Channels are automatically simplified by: 1. Removing bits e_i that do "
"not affect any f-variable (i.e. all-zero columns in error_transform) 2. "
"Merging channels with identical column signatures, i.e. channels whose "
"corresponding"
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler:13
msgid "columns in error_transform are identical."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler:14
msgid ""
"Absorbing channels whose signatures are subsets of others, i.e. channels "
"whose corresponding"
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler:15
msgid ""
"columns in error_transform are a strict subset of another channel's "
"columns."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler.__init__:1
msgid ""
"Initialize the sampler with channel probabilities and a basis "
"transformation."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler.__init__:3
#, python-brace-format
msgid ""
"List of probability arrays. Channel i has shape (2^k_i,) and produces k_i"
" error bits starting at index sum(k_0:k_{i-1}). For example, if channels "
"have shapes [(4,), (2,), (4,)], they produce variables [e0,e1], [e2], "
"[e3,e4]."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler.__init__:7
msgid ""
"Binary matrix of shape (num_f, num_e) where entry [i, j] = 1 means f_i "
"depends on e_j. For example, if row 0 is [0, 1, 0, 1], then f0 = e1 XOR "
"e3."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler.__init__:10
msgid "Random seed for sampling. If None, a random seed is generated."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler.num_f_params:1
msgid "Number of reduced basis parameters (f-basis)."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler.num_f_params:3
msgid "Number of parameters."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler.sample:1
msgid "Sample from all error channels and transform to the f-basis."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler.sample:3
msgid "Uses geometric-skip sampling optimized for low-noise regimes."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler.sample:7
msgid "Binary array of shape (num_samples, num_f) containing sampled outcomes."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler.sample_jax:1
msgid "Sample from all error channels on-device using JAX categorical sampling."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler.sample_jax:3
msgid "Number of samples to draw."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler.sample_jax:5
msgid "JAX PRNG key."
msgstr ""

#: of tensorcircuit.zx.noise_model.ChannelSampler.sample_jax:7
msgid ""
"Tuple of (samples, next_key) where samples is a JAX array of shape "
"(num_samples, num_f) with dtype uint8."
msgstr ""

#: of tensorcircuit.zx.noise_model.absorb_subset_channels:1
msgid "Absorb channels whose signatures are subsets of others."
msgstr ""

#: of tensorcircuit.zx.noise_model.absorb_subset_channels:3
msgid ""
"If channel A's signatures are a strict subset of channel B's signatures, "
"and |B| <= max_bits, then A is absorbed into B."
msgstr ""

#: of tensorcircuit.zx.noise_model.absorb_subset_channels:6
#: tensorcircuit.zx.noise_model.merge_identical_channels:6
#: tensorcircuit.zx.noise_model.normalize_channels:6
#: tensorcircuit.zx.noise_model.reduce_null_bits:7
msgid "List of channels"
msgstr ""

#: of tensorcircuit.zx.noise_model.absorb_subset_channels:7
msgid "Maximum number of bits allowed per channel"
msgstr ""

#: of tensorcircuit.zx.noise_model.absorb_subset_channels:9
msgid "List with no channel being a strict subset of another"
msgstr ""

#: of tensorcircuit.zx.noise_model.correlated_error_probs:1
msgid "Build probability distribution for correlated error chain."
msgstr ""

#: of tensorcircuit.zx.noise_model.correlated_error_probs:3
msgid ""
"Given conditional probabilities [p1, p2, ..., pk] from a chain of "
"CORRELATED_ERROR(p1) ELSE_CORRELATED_ERROR(p2) ... "
"ELSE_CORRELATED_ERROR(pk), computes the joint probability distribution "
"over 2^k outcomes."
msgstr ""

#: of tensorcircuit.zx.noise_model.correlated_error_probs:7
#, python-brace-format
msgid ""
"Since errors are mutually exclusive, only outcomes with at most one bit "
"set have non-zero probability: - P(0) = (1-p1)(1-p2)...(1-pk)  (no error)"
" - P(2^i) = (1-p1)...(1-p_i) * p_{i+1}  (error i+1 occurred)"
msgstr ""

#: of tensorcircuit.zx.noise_model.correlated_error_probs:12
msgid "List of conditional probabilities [p1, p2, ..., pk]"
msgstr ""

#: of tensorcircuit.zx.noise_model.correlated_error_probs:14
msgid "Array of shape (2^k,) with probabilities for each outcome."
msgstr ""

#: of tensorcircuit.zx.noise_model.error_probs:1
msgid "Single-bit error channel probability distribution."
msgstr ""

#: of tensorcircuit.zx.noise_model.error_probs:3
msgid "Error probability."
msgstr ""

#: of tensorcircuit.zx.noise_model.error_probs:5
msgid "Array [1-p, p] of shape (2,)."
msgstr ""

#: of tensorcircuit.zx.noise_model.expand_channel:1
msgid "Expand a channel's distribution to a larger signature set."
msgstr ""

#: of tensorcircuit.zx.noise_model.expand_channel:3
msgid ""
"The channel's existing col_ids must be a strict subset of target_col_ids."
" Both must be sorted. New bit positions are treated as \"don't care\" "
"(always 0)."
msgstr ""

#: of tensorcircuit.zx.noise_model.expand_channel:6
msgid "Channel to expand (must have sorted unique_col_ids)"
msgstr ""

#: of tensorcircuit.zx.noise_model.expand_channel:7
msgid "Target signature set (must be sorted superset)"
msgstr ""

#: of tensorcircuit.zx.noise_model.expand_channel:9
msgid "New channel with expanded distribution"
msgstr ""

#: of tensorcircuit.zx.noise_model.merge_identical_channels:1
msgid "Merge all channels with identical signature sets."
msgstr ""

#: of tensorcircuit.zx.noise_model.merge_identical_channels:3
msgid ""
"Groups channels by their unique_col_ids and convolves all channels in "
"each group into a single channel."
msgstr ""

#: of tensorcircuit.zx.noise_model.merge_identical_channels:8
msgid "List with at most one channel per unique signature set"
msgstr ""

#: of tensorcircuit.zx.noise_model.normalize_channels:1
msgid "Normalize channels by sorting unique_col_ids, permuting probs accordingly."
msgstr ""

#: of tensorcircuit.zx.noise_model.normalize_channels:3
msgid ""
"This ensures channels affecting the same set of columns have identical "
"unique_col_ids tuples, enabling merge_identical_channels to group them."
msgstr ""

#: of tensorcircuit.zx.noise_model.normalize_channels:8
msgid "List of channels with sorted unique_col_ids"
msgstr ""

#: of tensorcircuit.zx.noise_model.pauli_channel_1_probs:1
msgid "Single-qubit Pauli channel probability distribution."
msgstr ""

#: of tensorcircuit.zx.noise_model.pauli_channel_1_probs:3
msgid "X error probability."
msgstr ""

#: of tensorcircuit.zx.noise_model.pauli_channel_1_probs:5
msgid "Y error probability."
msgstr ""

#: of tensorcircuit.zx.noise_model.pauli_channel_1_probs:7
msgid "Z error probability."
msgstr ""

#: of tensorcircuit.zx.noise_model.pauli_channel_1_probs:9
msgid "Array [I, Z, X, Y] of shape (4,)."
msgstr ""

#: of tensorcircuit.zx.noise_model.pauli_channel_2_probs:1
msgid "Two-qubit Pauli channel probability distribution."
msgstr ""

#: of tensorcircuit.zx.noise_model.pauli_channel_2_probs:3
msgid "Probability of error I on qubit 1 and X on qubit 2."
msgstr ""

#: of tensorcircuit.zx.noise_model.pauli_channel_2_probs:4
msgid "... :return: Array of shape (16,). :rtype: np.ndarray"
msgstr ""

#: of tensorcircuit.zx.noise_model.reduce_null_bits:1
msgid "Remove bits corresponding to the null column (all-zero column)."
msgstr ""

#: of tensorcircuit.zx.noise_model.reduce_null_bits:3
msgid ""
"If a channel has bits mapped to null_col_id (representing an all-zero "
"column in the transform matrix), those bits don't affect any f-variable "
"and can be marginalized out by summing over them."
msgstr ""

#: of tensorcircuit.zx.noise_model.reduce_null_bits:8
msgid ""
"Column ID representing the all-zero column, or None if there is no all-"
"zero column."
msgstr ""

#: of tensorcircuit.zx.noise_model.reduce_null_bits:11
msgid ""
"List of channels with null bits marginalized out. Channels with all null "
"entries are removed entirely (they have no effect on outputs)."
msgstr ""

#: of tensorcircuit.zx.noise_model.simplify_channels:1
msgid ""
"Simplify a list of channels by reducing null bits, merging identical "
"signatures, and absorbing subsets."
msgstr ""

#: of tensorcircuit.zx.noise_model.simplify_channels:3
msgid "List of input channels."
msgstr ""

#: of tensorcircuit.zx.noise_model.simplify_channels:5
msgid "Maximum number of bits allowed per simplified channel, defaults to 4."
msgstr ""

#: of tensorcircuit.zx.noise_model.simplify_channels:7
msgid "ID of the all-zero column in the error transform, defaults to None."
msgstr ""

#: of tensorcircuit.zx.noise_model.simplify_channels:9
msgid "Simplified list of channels."
msgstr ""

#: of tensorcircuit.zx.noise_model.xor_convolve:1
msgid "XOR convolution of two probability distributions."
msgstr ""

#: of tensorcircuit.zx.noise_model.xor_convolve:3
msgid "Probability distribution of channel A."
msgstr ""

#: of tensorcircuit.zx.noise_model.xor_convolve:5
msgid "Probability distribution of channel B."
msgstr ""

#: of tensorcircuit.zx.noise_model.xor_convolve:7
msgid "Combined probability distribution."
msgstr ""

#: ../../source/api/zx/scalar_graph.rst:2
msgid "tensorcircuit.zx.scalar_graph"
msgstr ""

#: of tensorcircuit.zx.scalar_graph:1
msgid "Decomposition of ZX graphs into scalar graphs and JAX-compatible IR."
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledComponent.compiled_scalar_graphs:1
#: tensorcircuit.zx.scalar_graph.CompiledProgram.output_order:1
#: tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.a_const_phases:1
msgid "Alias for field number 2"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledProgram.mode:1
#: tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.a_param_bits:1
msgid "Alias for field number 3"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.a_num_terms:1
msgid "Alias for field number 4"
msgstr ""

#: of
#: tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.approximate_floatfactors:1
msgid "Alias for field number 18"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.b_param_bits:1
msgid "Alias for field number 6"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.b_term_types:1
msgid "Alias for field number 5"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.c_const_bits_a:1
msgid "Alias for field number 7"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.c_const_bits_b:1
msgid "Alias for field number 9"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.c_param_bits_a:1
msgid "Alias for field number 8"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.c_param_bits_b:1
msgid "Alias for field number 10"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.d_const_alpha:1
msgid "Alias for field number 11"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.d_const_beta:1
msgid "Alias for field number 12"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.d_num_terms:1
msgid "Alias for field number 15"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.d_param_bits_a:1
msgid "Alias for field number 13"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.d_param_bits_b:1
msgid "Alias for field number 14"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.floatfactor:1
msgid "Alias for field number 20"
msgstr ""

#: of
#: tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.has_approximate_floatfactors:1
msgid "Alias for field number 17"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.phase_indices:1
msgid "Alias for field number 16"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.CompiledScalarGraphs.power2:1
msgid "Alias for field number 19"
msgstr ""

#: of tensorcircuit.zx.scalar_graph.compile_program:1
msgid "Compile a prepared sampling graph into a multicomponent program."
msgstr ""

#: of tensorcircuit.zx.scalar_graph.compile_program:3
msgid "The prepared sampling graph metadata."
msgstr ""

#: of tensorcircuit.zx.scalar_graph.compile_program:5
msgid "Compilation mode, currently only 'sequential' is supported."
msgstr ""

#: of tensorcircuit.zx.scalar_graph.compile_program:7
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.__init__:7
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.from_circuit:5
msgid "Decomposition strategy for T gates, defaults to \"cat5\"."
msgstr ""

#: of tensorcircuit.zx.scalar_graph.compile_program:9
msgid "The compiled program metadata."
msgstr ""

#: of tensorcircuit.zx.scalar_graph.compile_scalar_graphs:1
msgid "Compile a list of ZX scalar graphs into a JAX-compatible IR format."
msgstr ""

#: of tensorcircuit.zx.scalar_graph.compile_scalar_graphs:3
msgid "List of ZX scalar graphs."
msgstr ""

#: of tensorcircuit.zx.scalar_graph.compile_scalar_graphs:5
msgid "List of parameter names (e.g., 'f0', 'm0')."
msgstr ""

#: of tensorcircuit.zx.scalar_graph.compile_scalar_graphs:7
msgid "Compiled scalar graphs in IR format."
msgstr ""

#: of tensorcircuit.zx.scalar_graph.find_stab:1
msgid "Decompose a ZX graph into a sum of stabilizer graphs."
msgstr ""

#: of tensorcircuit.zx.scalar_graph.find_stab:3
msgid "The ZX graph to decompose."
msgstr ""

#: of tensorcircuit.zx.scalar_graph.find_stab:5
msgid "Decomposition strategy for T gates."
msgstr ""

#: of tensorcircuit.zx.scalar_graph.find_stab:7
msgid "If True, skip the initial full_reduce (caller already reduced)."
msgstr ""

#: of tensorcircuit.zx.scalar_graph.find_stab:9
msgid "List of stabilizer graphs."
msgstr ""

#: ../../source/api/zx/simplifier.rst:2
msgid "tensorcircuit.zx.simplifier"
msgstr ""

#: of tensorcircuit.zx.simplifier:1
msgid "ZX graph simplification wrapper."
msgstr ""

#: of tensorcircuit.zx.simplifier.full_reduce:1
msgid "Apply PyZX full_reduce to the graph."
msgstr ""

#: of tensorcircuit.zx.simplifier.full_reduce:5
msgid "Whether to use parameter-safe simplification, defaults to True."
msgstr ""

#: of tensorcircuit.zx.simplifier.t_count:1
msgid "Return the T-count of the graph."
msgstr ""

#: of tensorcircuit.zx.simplifier.t_count:5
msgid "Number of T or T-like vertices."
msgstr ""

#: of tensorcircuit.zx.simplifier.teleport_reduce:1
msgid "Apply PyZX teleport_reduce to the graph."
msgstr ""

#: of tensorcircuit.zx.simplifier.teleport_reduce:5
msgid "A new simplified graph."
msgstr ""

#: ../../source/api/zx/stabilizertcircuit.rst:2
msgid "tensorcircuit.zx.stabilizertcircuit"
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit:1
msgid "Stabilizer+T Circuit class using ZX-calculus and JAX."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.__init__:1
msgid "Initialize a StabilizerTCircuit."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.amplitude:1
msgid ""
"Calculate the complex amplitude <state|psi> for a noiseless circuit. "
"Fails if the circuit contains non-unitary operations or noise."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.amplitude:4
msgid "The target bitstring."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.amplitude:6
msgid "The complex amplitude."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.expectation_ps:1
msgid ""
"Calculate the expectation value <psi|O|psi> for a Pauli string O. "
"Supports noiseless and noisy circuits."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.expectation_ps:4
msgid "Qubit indices for X operators."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.expectation_ps:6
msgid "Qubit indices for Y operators."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.expectation_ps:8
msgid "Qubit indices for Z operators."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.expectation_ps:10
msgid "Pauli string as a sequence of integers (0:I, 1:X, 2:Y, 3:Z)."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.expectation_ps:12
msgid "Number of Monte Carlo trajectories for noisy circuits."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.expectation_ps:14
msgid "The expectation value."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.from_circuit:1
msgid ""
"Create a StabilizerTCircuit from an existing TensorCircuit "
"AbstractCircuit."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.from_circuit:3
msgid "The source circuit to convert."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.from_circuit:7
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.from_stim_circuit:5
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.from_stim_str:5
msgid "A new StabilizerTCircuit instance."
msgstr ""

#: of
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.from_stim_circuit:1
msgid "Create a StabilizerTCircuit from a stim.Circuit object."
msgstr ""

#: of
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.from_stim_circuit:3
msgid "The stim circuit to convert."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.from_stim_str:1
msgid "Create a StabilizerTCircuit from a stim circuit string."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.from_stim_str:3
msgid "The stim circuit string to parse."
msgstr ""

#: of
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.outcome_probability:1
msgid "Compute the probability of a specific measurement outcome state."
msgstr ""

#: of
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.outcome_probability:3
msgid "The target measurement bitstring."
msgstr ""

#: of
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.outcome_probability:5
msgid "Number of noise realizations to average over, defaults to 1."
msgstr ""

#: of
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.outcome_probability:7
msgid "Probability of the outcome state for each noise realization."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.sample_detectors:1
msgid "Sample detector and observable outcomes."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.sample_detectors:5
msgid "Whether to return detectors and observables separately, defaults to False."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.sample_detectors:7
msgid "Whether to XOR results with a noiseless reference run, defaults to False."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.sample_detectors:9
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.sample_measurements:5
msgid "Random seed for this sampling run, defaults to None."
msgstr ""

#: of
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.sample_detectors:11
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.sample_measurements:7
msgid "Number of shots per JIT batch, defaults to 1000."
msgstr ""

#: of
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.sample_detectors:13
msgid "Array of samples or tuple of (detectors, observables) arrays."
msgstr ""

#: of
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.sample_measurements:1
msgid "Sample all measurement outcomes in the circuit."
msgstr ""

#: of
#: tensorcircuit.zx.stabilizertcircuit.StabilizerTCircuit.sample_measurements:9
msgid "Array of measurement samples with shape (shots, num_measurements)."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.sample_component:1
msgid "Sample measurement outcomes for a single compiled component."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.sample_component:3
msgid "The compiled component to sample from."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.sample_component:5
#: tensorcircuit.zx.stabilizertcircuit.sample_program:5
msgid "The sampled error bit parameters (f-basis)."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.sample_component:7
#: tensorcircuit.zx.stabilizertcircuit.sample_program:7
msgid "JAX PRNG key for sampling."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.sample_component:9
msgid ""
"A tuple containing the measurement samples, next PRNG key, and maximum "
"deviation."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.sample_program:1
msgid "Sample measurement outcomes for an entire compiled program."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.sample_program:3
msgid "The compiled program to sample from."
msgstr ""

#: of tensorcircuit.zx.stabilizertcircuit.sample_program:9
msgid "The measurement samples for all qubits."
msgstr ""

#: ../../source/api/zx/utils.rst:2
msgid "tensorcircuit.zx.utils"
msgstr ""

#: of tensorcircuit.zx.utils:1
msgid "Utility functions for ZX-calculus and stabilizer decomposition."
msgstr ""

#: of tensorcircuit.zx.utils.ConnectedComponent:1
msgid "A connected subgraph with its associated output indices."
msgstr ""

#: of tensorcircuit.zx.utils.connected_components:1
msgid "Return each connected component of ``g`` as its own ZX subgraph."
msgstr ""

#: of tensorcircuit.zx.utils.connected_components:3
msgid "The input ZX graph."
msgstr ""

#: of tensorcircuit.zx.utils.connected_components:5
msgid "List of ConnectedComponent objects."
msgstr ""

#: of tensorcircuit.zx.utils.find_basis:1
msgid ""
"Decompose a set of binary vectors into a basis subset and a "
"transformation matrix over GF(2)."
msgstr ""

#: of tensorcircuit.zx.utils.find_basis:3
msgid "Input binary vectors (2D array-like)."
msgstr ""

#: of tensorcircuit.zx.utils.find_basis:5
msgid "Tuple of (basis_vectors, transformation_matrix)."
msgstr ""

#: of tensorcircuit.zx.utils.get_params:1
msgid "Get all parameter variables that appear in the graph and its scalar."
msgstr ""

#: of tensorcircuit.zx.utils.get_params:3
msgid "The ZX graph to inspect."
msgstr ""

#: of tensorcircuit.zx.utils.get_params:5
msgid "A set of parameter names."
msgstr ""

#~ msgid ""
#~ "This is a method that implementers "
#~ "of subclasses of `Layer` or `Model` "
#~ "can override if they need a "
#~ "state-creation step in-between layer "
#~ "instantiation and layer call."
#~ msgstr ""

#~ msgid "This is typically used to create the weights of `Layer` subclasses."
#~ msgstr ""

#~ msgid ""
#~ "Note here that `call()` method in "
#~ "`tf.keras` is little bit different from"
#~ " `keras` API. In `keras` API, you "
#~ "can pass support masking for layers "
#~ "as additional arguments. Whereas `tf.keras`"
#~ " has `compute_mask()` method to support "
#~ "masking."
#~ msgstr ""

#~ msgid "Modules for DQAS framework"
#~ msgstr ""

#~ msgid "DQAS framework entrypoint"
#~ msgstr ""

#~ msgid "Parameters"
#~ msgstr ""

#~ msgid ""
#~ "function with input of data instance,"
#~ " circuit parameters theta and structural"
#~ " paramter k, return tuple of "
#~ "objective value and gradient with "
#~ "respect to theta"
#~ msgstr ""

#~ msgid "data generator as dataset"
#~ msgstr ""

#~ msgid "list of operations as primitive operator pool"
#~ msgstr ""

#~ msgid "the default layer number of the circuit ansatz"
#~ msgstr ""

#~ msgid ""
#~ "shape of circuit parameter pool, in "
#~ "general p_stp*l, where l is the "
#~ "max number of circuit parameters for "
#~ "op in the operator pool"
#~ msgstr ""

#~ msgid "the same as p in the most times"
#~ msgstr ""

#~ msgid "batch size of one epoch"
#~ msgstr ""

#~ msgid "prethermal update times"
#~ msgstr ""

#~ msgid "training epochs"
#~ msgstr ""

#~ msgid "parallel thread number, 0 to disable multiprocessing model by default"
#~ msgstr ""

#~ msgid "set verbose log to print"
#~ msgstr ""

#~ msgid "function to output verbose information"
#~ msgstr ""

#~ msgid "function return intermiediate result for final history list"
#~ msgstr ""

#~ msgid "cutoff probability to avoid peak distribution"
#~ msgstr ""

#~ msgid ""
#~ "function accepting list of objective "
#~ "values and return the baseline value "
#~ "used in the next round"
#~ msgstr ""

#~ msgid "return noise with the same shape as circuit parameter pool"
#~ msgstr ""

#~ msgid "initial values for circuit parameter pool"
#~ msgstr ""

#~ msgid "initial values for probabilistic model parameters"
#~ msgstr ""

#~ msgid "optimizer for circuit parameters theta"
#~ msgstr ""

#~ msgid "optimizer for model parameters alpha"
#~ msgstr ""

#~ msgid "optimizer for circuit parameters in prethermal stage"
#~ msgstr ""

#~ msgid "fixed structural parameters for prethermal training"
#~ msgstr ""

#~ msgid "regularization function for model parameters alpha"
#~ msgstr ""

#~ msgid "regularization function for circuit parameters theta"
#~ msgstr ""

#~ msgid "Returns"
#~ msgstr ""

#~ msgid ""
#~ "The probabilistic model based DQAS, can"
#~ " use extensively for DQAS case for"
#~ " ``NMF`` probabilistic model."
#~ msgstr ""

#~ msgid "vag func, return loss and nabla lnp"
#~ msgstr ""

#~ msgid "keras model"
#~ msgstr ""

#~ msgid "sample func of logic with keras model input"
#~ msgstr ""

#~ msgid "input data pipeline generator"
#~ msgstr ""

#~ msgid "operation pool"
#~ msgstr ""

#~ msgid "depth for DQAS"
#~ msgstr ""

#~ msgid "parallel kernels"
#~ msgstr ""

#~ msgid "final loss function in terms of average of sub loss for each circuit"
#~ msgstr ""

#~ msgid "derivative function for ``loss_func``"
#~ msgstr ""

#~ msgid ""
#~ "Call in customized functions and grab"
#~ " variables within DQAS framework function"
#~ " by var name str."
#~ msgstr ""

#~ msgid "The DQAS framework function"
#~ msgstr ""

#~ msgid "Variables within the DQAS framework"
#~ msgstr ""

#~ msgid "Return type"
#~ msgstr ""

#~ msgid ""
#~ "This function works only when nnp "
#~ "has the same shape as stp, i.e."
#~ " one parameter for each op."
#~ msgstr ""

#~ msgid "The kernel for multiprocess to run parallel in DQAS function/"
#~ msgstr ""

#~ msgid ""
#~ "parallel variational parameter training and"
#~ " search to avoid local minimum not"
#~ " limited to qaoa setup as the "
#~ "function name indicates, as long as "
#~ "you provided suitable `vag_func`"
#~ msgstr ""

#~ msgid "data input generator for vag_func"
#~ msgstr ""

#~ msgid "vag_kernel"
#~ msgstr ""

#~ msgid "number of tries"
#~ msgstr ""

#~ msgid ""
#~ "for optimization problem the input is"
#~ " in general fixed so batch is "
#~ "often 1"
#~ msgstr ""

#~ msgid "number of parallel jobs"
#~ msgstr ""

#~ msgid "mean value of normal distribution for nnp"
#~ msgstr ""

#~ msgid "std deviation of normal distribution for nnp"
#~ msgstr ""

#~ msgid "Doesn't support prob model DQAS search."
#~ msgstr ""

#~ msgid "Modules for graph instance data and more"
#~ msgstr ""

#~ msgid "```python d = nx.to_dict_of_dicts(g) ```"
#~ msgstr ""

#~ msgid "1D PBC chain with n sites."
#~ msgstr ""

#~ msgid "The number of nodes"
#~ msgstr ""

#~ msgid "The resulted graph g"
#~ msgstr ""

#~ msgid "all graphs with m edge out from g"
#~ msgstr ""

#~ msgid ""
#~ "Generate a reduced graph with given "
#~ "ratio of edges compared to the "
#~ "original graph g."
#~ msgstr ""

#~ msgid "The base graph"
#~ msgstr ""

#~ msgid "number of edges kept, default half of the edges"
#~ msgstr ""

#~ msgid "The resulted reduced graph"
#~ msgstr ""

#~ msgid "Split the graph in exactly ``split`` piece evenly."
#~ msgstr ""

#~ msgid "The mother graph"
#~ msgstr ""

#~ msgid "The number of the graph we want to divide into, defaults to 2"
#~ msgstr ""

#~ msgid "List of graph instance of size ``split``"
#~ msgstr ""

#~ msgid "Module for functions adding layers of circuits"
#~ msgstr ""

#~ msgid "Hlayer"
#~ msgstr ""

#~ msgid "anyrxlayer"
#~ msgstr ""

#~ msgid "anyrylayer"
#~ msgstr ""

#~ msgid "anyrzlayer"
#~ msgstr ""

#~ msgid "anyswaplayer"
#~ msgstr ""

#~ msgid "anyxxlayer"
#~ msgstr ""

#~ msgid "anyxylayer"
#~ msgstr ""

#~ msgid "anyxzlayer"
#~ msgstr ""

#~ msgid "anyyxlayer"
#~ msgstr ""

#~ msgid "anyyylayer"
#~ msgstr ""

#~ msgid "anyyzlayer"
#~ msgstr ""

#~ msgid "anyzxlayer"
#~ msgstr ""

#~ msgid "anyzylayer"
#~ msgstr ""

#~ msgid "anyzzlayer"
#~ msgstr ""

#~ msgid "cnotlayer"
#~ msgstr ""

#~ msgid "rxlayer"
#~ msgstr ""

#~ msgid "rylayer"
#~ msgstr ""

#~ msgid "rzlayer"
#~ msgstr ""

#~ msgid "swaplayer"
#~ msgstr ""

#~ msgid "xxgate"
#~ msgstr ""

#~ msgid "xxlayer"
#~ msgstr ""

#~ msgid "xygate"
#~ msgstr ""

#~ msgid "xylayer"
#~ msgstr ""

#~ msgid "xzgate"
#~ msgstr ""

#~ msgid "xzlayer"
#~ msgstr ""

#~ msgid "yxgate"
#~ msgstr ""

#~ msgid "yxlayer"
#~ msgstr ""

#~ msgid "yygate"
#~ msgstr ""

#~ msgid "yylayer"
#~ msgstr ""

#~ msgid "yzgate"
#~ msgstr ""

#~ msgid "yzlayer"
#~ msgstr ""

#~ msgid "zxgate"
#~ msgstr ""

#~ msgid "zxlayer"
#~ msgstr ""

#~ msgid "zygate"
#~ msgstr ""

#~ msgid "zylayer"
#~ msgstr ""

#~ msgid "zzgate"
#~ msgstr ""

#~ msgid "zzlayer"
#~ msgstr ""

#~ msgid "$$e^{-i     heta_i \\sigma}$$"
#~ msgstr ""

#~ msgid ""
#~ "The following function should be used"
#~ " to generate layers with special "
#~ "case. As its soundness depends on "
#~ "the nature of the task or problem,"
#~ " it doesn't always make sense."
#~ msgstr ""

#~ msgid "$$e^{-i heta \\sigma}$$"
#~ msgstr ""

#~ msgid "$$e^{-i     heta \\sigma}$$"
#~ msgstr ""

#~ msgid ""
#~ "A collection of useful function snippets"
#~ " that irrelevant with the main "
#~ "modules or await for furthere refactor"
#~ msgstr ""

#~ msgid "Bases: :py:class:`object`"
#~ msgstr ""

#~ msgid ""
#~ "color cirq circuit SVG for given "
#~ "gates, a small tool to hack the"
#~ " cirq SVG"
#~ msgstr ""

#~ msgid "integer coordinate which gate is colored"
#~ msgstr ""

#~ msgid "transform repr form of an array to real numpy array"
#~ msgstr ""

#~ msgid "DQAS application kernels as vag functions"
#~ msgstr ""

#~ msgid "1D array for full wavefunction, the basis is in lexcical order"
#~ msgstr ""

#~ msgid "nx.Graph"
#~ msgstr ""

#~ msgid "transformation functions before averaged"
#~ msgstr ""

#~ msgid "as f3"
#~ msgstr ""

#~ msgid "maxcut energy for n qubit wavefunction i-th basis"
#~ msgstr ""

#~ msgid "ranged from 0 to 2**n-1"
#~ msgstr ""

#~ msgid "number of qubits"
#~ msgstr ""

#~ msgid ""
#~ "deprecated as non tf and non "
#~ "flexible, use the combination of "
#~ "``reduced_density_matrix`` and ``entropy`` instead."
#~ msgstr ""

#~ msgid "deprecated, current version in tc.quantum"
#~ msgstr ""

#~ msgid ""
#~ "value and gradient, currently only "
#~ "tensorflow backend is supported jax and"
#~ " numpy seems to be slow in "
#~ "circuit simulation anyhow. *deprecated*"
#~ msgstr ""

#~ msgid "if lbd=0, take energy as objective"
#~ msgstr ""

#~ msgid "if as default 0, overlap will not compute in the process"
#~ msgstr ""

#~ msgid "Fill single qubit gates according to placeholder on circuit"
#~ msgstr ""

#~ msgid "Hamiltonian measurements for Heisenberg model on graph lattice g"
#~ msgstr ""

#~ msgid "short cut for ``cirq.LineQubit(i)``"
#~ msgstr ""

#~ msgid "QAOA block encoding kernel, support 2 params in one op"
#~ msgstr ""

#~ msgid ""
#~ "training QAOA with only optimizing "
#~ "circuit parameters, can be well replaced"
#~ " with more general function `DQAS_search`"
#~ msgstr ""

#~ msgid "multi parameter for one layer"
#~ msgstr ""

#~ msgid "kw arguments for measurements_func"
#~ msgstr ""

#~ msgid "loss function, gradient of nnp"
#~ msgstr ""

#~ msgid ""
#~ "tensorflow quantum backend compare to "
#~ "qaoa_vag which is tensorcircuit backend"
#~ msgstr ""

#~ msgid "Hamiltonian for tfim on lattice defined by graph g"
#~ msgstr ""

#~ msgid "cirq.PauliSum as operators for tfq expectation layer"
#~ msgstr ""

#~ msgid ""
#~ "generate random wavefunction from "
#~ "approximately Haar measure, reference:  "
#~ "https://doi.org/10.1063/1.4983266"
#~ msgstr ""

#~ msgid "repetition of the blocks"
#~ msgstr ""

#~ msgid "random Haar measure approximation"
#~ msgstr ""

#~ msgid "cirq.Circuit, empty circuit"
#~ msgstr ""

#~ msgid "# of qubit"
#~ msgstr ""

#~ msgid ""
#~ "One-hot variational autoregressive models "
#~ "for multiple categorical choices beyond "
#~ "binary"
#~ msgstr ""

#~ msgid "Bases: :py:class:`keras.engine.training.Model`"
#~ msgstr ""

#~ msgid "Calls the model on new inputs and returns the outputs as tensors."
#~ msgstr ""

#~ msgid ""
#~ "In this case `call()` just reapplies "
#~ "all ops in the graph to the "
#~ "new inputs (e.g. build a new "
#~ "computational graph from the provided "
#~ "inputs)."
#~ msgstr ""

#~ msgid ""
#~ "Note: This method should not be "
#~ "called directly. It is only meant "
#~ "to be overridden when subclassing "
#~ "`tf.keras.Model`. To call a model on "
#~ "an input, always use the `__call__()`"
#~ " method, i.e. `model(inputs)`, which relies"
#~ " on the underlying `call()` method."
#~ msgstr ""

#~ msgid "Args:"
#~ msgstr ""

#~ msgid ""
#~ "inputs: Input tensor, or dict/list/tuple "
#~ "of input tensors. training: Boolean or"
#~ " boolean scalar tensor, indicating whether"
#~ " to run"
#~ msgstr ""

#~ msgid "the `Network` in training mode or inference mode."
#~ msgstr ""

#~ msgid "mask: A mask or list of masks. A mask can be either a boolean tensor or"
#~ msgstr ""

#~ msgid "None (no mask). For more details, check the guide"
#~ msgstr ""

#~ msgid "[here](https://www.tensorflow.org/guide/keras/masking_and_padding)."
#~ msgstr ""

#~ msgid "Returns:"
#~ msgstr ""

#~ msgid ""
#~ "A tensor if there is a single "
#~ "output, or a list of tensors if"
#~ " there are more than one outputs."
#~ msgstr ""

#~ msgid "Bases: :py:class:`keras.engine.base_layer.Layer`"
#~ msgstr ""

#~ msgid ""
#~ "Creates the variables of the layer "
#~ "(optional, for subclass implementers)."
#~ msgstr ""

#~ msgid ""
#~ "This is a method that implementers "
#~ "of subclasses of `Layer` or `Model` "
#~ "can override if they need a "
#~ "state-creation step in-between layer "
#~ "instantiation and layer call. It is "
#~ "invoked automatically before the first "
#~ "execution of `call()`."
#~ msgstr ""

#~ msgid ""
#~ "This is typically used to create "
#~ "the weights of `Layer` subclasses (at"
#~ " the discretion of the subclass "
#~ "implementer)."
#~ msgstr ""

#~ msgid "input_shape: Instance of `TensorShape`, or list of instances of"
#~ msgstr ""

#~ msgid ""
#~ "`TensorShape` if the layer expects a "
#~ "list of inputs (one instance per "
#~ "input)."
#~ msgstr ""

#~ msgid "This is where the layer's logic lives."
#~ msgstr ""

#~ msgid ""
#~ "The `call()` method may not create "
#~ "state (except in its first invocation,"
#~ " wrapping the creation of variables "
#~ "or other resources in `tf.init_scope()`). "
#~ "It is recommended to create state "
#~ "in `__init__()`, or the `build()` method"
#~ " that is called automatically before "
#~ "`call()` executes the first time."
#~ msgstr ""

#~ msgid "inputs: Input tensor, or dict/list/tuple of input tensors."
#~ msgstr ""

#~ msgid ""
#~ "The first positional `inputs` argument "
#~ "is subject to special rules: - "
#~ "`inputs` must be explicitly passed. A"
#~ " layer cannot have zero"
#~ msgstr ""

#~ msgid ""
#~ "arguments, and `inputs` cannot be "
#~ "provided via the default value of "
#~ "a keyword argument."
#~ msgstr ""

#~ msgid "NumPy array or Python scalar values in `inputs` get cast as tensors."
#~ msgstr ""

#~ msgid "Keras mask metadata is only collected from `inputs`."
#~ msgstr ""

#~ msgid ""
#~ "Layers are built (`build(input_shape)` method)"
#~ " using shape info from `inputs` only."
#~ msgstr ""

#~ msgid "`input_spec` compatibility is only checked against `inputs`."
#~ msgstr ""

#~ msgid ""
#~ "Mixed precision input casting is only"
#~ " applied to `inputs`. If a layer "
#~ "has tensor arguments in `*args` or "
#~ "`**kwargs`, their casting behavior in "
#~ "mixed precision should be handled "
#~ "manually."
#~ msgstr ""

#~ msgid "The SavedModel input specification is generated using `inputs` only."
#~ msgstr ""

#~ msgid ""
#~ "Integration with various ecosystem packages"
#~ " like TFMOT, TFLite, TF.js, etc is"
#~ " only supported for `inputs` and not"
#~ " for tensors in positional and "
#~ "keyword arguments."
#~ msgstr ""

#~ msgid "*args: Additional positional arguments. May contain tensors, although"
#~ msgstr ""

#~ msgid "this is not recommended, for the reasons above."
#~ msgstr ""

#~ msgid "**kwargs: Additional keyword arguments. May contain tensors, although"
#~ msgstr ""

#~ msgid ""
#~ "this is not recommended, for the "
#~ "reasons above. The following optional "
#~ "keyword arguments are reserved: - "
#~ "`training`: Boolean scalar tensor of "
#~ "Python boolean indicating"
#~ msgstr ""

#~ msgid "whether the `call` is meant for training or inference."
#~ msgstr ""

#~ msgid ""
#~ "`mask`: Boolean input mask. If the "
#~ "layer's `call()` method takes a `mask`"
#~ " argument, its default value will be"
#~ " set to the mask generated for "
#~ "`inputs` by the previous layer (if "
#~ "`input` did come from a layer that"
#~ " generated a corresponding mask, i.e. "
#~ "if it came from a Keras layer "
#~ "with masking support)."
#~ msgstr ""

#~ msgid "A tensor or list/tuple of tensors."
#~ msgstr ""

#~ msgid "Relevant classes for VQNHE"
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`keras.optimizer_v2.learning_rate_schedule.LearningRateSchedule`"
#~ msgstr ""

#~ msgid "Dense layer but with complex weights, used for building complex RBM"
#~ msgstr ""

#~ msgid "VQNHE"
#~ msgstr ""

#~ msgid "[description]"
#~ msgstr ""

#~ msgid "VQE"
#~ msgstr ""

#~ msgid "Backend register"
#~ msgstr ""

#~ msgid "Get the `tc.backend` object."
#~ msgstr ""

#~ msgid "\"numpy\", \"tensorflow\", \"jax\", \"pytorch\""
#~ msgstr ""

#~ msgid "Raises"
#~ msgstr ""

#~ msgid "Backend doesn't exist for `backend` argument."
#~ msgstr ""

#~ msgid "The `tc.backend` object that with all registered universal functions."
#~ msgstr ""

#~ msgid "Backend magic inherited from tensornetwork: jax backend"
#~ msgstr ""

#~ msgid "Bases: :py:class:`tensornetwork.backends.jax.jax_backend.JaxBackend`"
#~ msgstr ""

#~ msgid ""
#~ "See the original backend API at "
#~ "``jax backend``. "
#~ "<https://github.com/google/TensorNetwork/blob/master/tensornetwork/backends/jax/jax_backend.py>`_"
#~ msgstr ""

#~ msgid "Returns the elementwise absolute value of tensor. Args:"
#~ msgstr ""

#~ msgid "tensor: An input tensor."
#~ msgstr ""

#~ msgid "tensor: Its elementwise absolute value."
#~ msgstr ""

#~ msgid "Return the index of maximum of an array an axis."
#~ msgstr ""

#~ msgid "[description], defaults to 0, different behavior from numpy defaults!"
#~ msgstr ""

#~ msgid "Return the index of minimum of an array an axis."
#~ msgstr ""

#~ msgid "Cast the tensor dtype of a ``a``."
#~ msgstr ""

#~ msgid "tensor"
#~ msgstr ""

#~ msgid "\"float32\", \"float64\", \"complex64\", \"complex128\""
#~ msgstr ""

#~ msgid "``a`` of new dtype"
#~ msgstr ""

#~ msgid "Join a sequence of arrays along an existing axis."
#~ msgstr ""

#~ msgid "[description], defaults to 0"
#~ msgstr ""

#~ msgid ""
#~ "The native cond for XLA compiling, "
#~ "wrapper for ``tf.cond`` and limited "
#~ "functionality of ``jax.lax.cond``."
#~ msgstr ""

#~ msgid "Convert a np.array or a tensor to a tensor type for the backend."
#~ msgstr ""

#~ msgid ""
#~ "Generate the coo format sparse matrix"
#~ " from indices and values, which is"
#~ " the only sparse format supported in"
#~ " different ML backends."
#~ msgstr ""

#~ msgid "shape [n, 2] for n non zero values in the returned matrix"
#~ msgstr ""

#~ msgid "shape [n]"
#~ msgstr ""

#~ msgid "Tuple[int, ...]"
#~ msgstr ""

#~ msgid "Return the expm of ``a``, matrix exponential."
#~ msgstr ""

#~ msgid "tensor in matrix form"
#~ msgstr ""

#~ msgid "matrix exponential of matrix ``a``"
#~ msgstr ""

#~ msgid "Return the cosine of a tensor ``a``."
#~ msgstr ""

#~ msgid "cosine of ``a``"
#~ msgstr ""

#~ msgid "Return the cumulative sum of the elements along a given axis."
#~ msgstr ""

#~ msgid ""
#~ "The default behavior is the same "
#~ "as numpy, different from tf/torch as "
#~ "cumsum of the flatten 1D array, "
#~ "defaults to None"
#~ msgstr ""

#~ msgid "Return the copy of tensor ''a''."
#~ msgstr ""

#~ msgid "Return an identity matrix of dimension `dim`"
#~ msgstr ""

#~ msgid ""
#~ "Depending on specific backends, `dim` "
#~ "has to be either an int (numpy,"
#~ " torch, tensorflow) or a `ShapeType` "
#~ "object (for block-sparse backends). "
#~ "Block-sparse behavior is currently not "
#~ "supported"
#~ msgstr ""

#~ msgid ""
#~ "N (int): The dimension of the "
#~ "returned matrix. dtype: The dtype of "
#~ "the returned matrix. M (int): The "
#~ "dimension of the returned matrix."
#~ msgstr ""

#~ msgid "Return the function which is the grad function of input ``f``."
#~ msgstr ""

#~ msgid "Example"
#~ msgstr ""

#~ msgid "the function to be differentiated"
#~ msgstr ""

#~ msgid ""
#~ "the position of args in ``f`` that"
#~ " are to be differentiated, defaults "
#~ "to be 0"
#~ msgstr ""

#~ msgid "the grad function of ``f`` with the same set of arguments as ``f``"
#~ msgstr ""

#~ msgid "Return 1.j in as a tensor compatible with the backend."
#~ msgstr ""

#~ msgid "\"complex64\" or \"complex128\""
#~ msgstr ""

#~ msgid "1.j tensor"
#~ msgstr ""

#~ msgid "Return the elementwise imaginary value of a tensor ``a``."
#~ msgstr ""

#~ msgid "imaginary value of ``a``"
#~ msgstr ""

#~ msgid "[summary]"
#~ msgstr ""

#~ msgid "The possible options"
#~ msgstr ""

#~ msgid "Sampling output shape"
#~ msgstr ""

#~ msgid ""
#~ "probability for each option in a, "
#~ "defaults to None, as equal probability"
#~ " distribution"
#~ msgstr ""

#~ msgid ""
#~ "Call the random normal function with "
#~ "the random state management behind the"
#~ " scene."
#~ msgstr ""

#~ msgid "[description], defaults to 1"
#~ msgstr ""

#~ msgid "[description], defaults to \"32\""
#~ msgstr ""

#~ msgid "Determine whether the type of input ``a`` is  ``sparse``."
#~ msgstr ""

#~ msgid "input matrix ``a``"
#~ msgstr ""

#~ msgid "a bool indicating whether the matrix ``a`` is sparse"
#~ msgstr ""

#~ msgid "Return a boolean on whether ``a`` is a tensor in backend package."
#~ msgstr ""

#~ msgid "a tensor to be determined"
#~ msgstr ""

#~ msgid "whether ``a`` is a tensor"
#~ msgstr ""

#~ msgid "Return the jitted version of function ``f``."
#~ msgstr ""

#~ msgid "function to be jitted"
#~ msgstr ""

#~ msgid ""
#~ "index of args that doesn't regarded "
#~ "as tensor, only work for jax "
#~ "backend"
#~ msgstr ""

#~ msgid ""
#~ "whether open XLA compilation, only works"
#~ " for tensorflow backend, defaults False "
#~ "since several ops has no XLA "
#~ "correspondence"
#~ msgstr ""

#~ msgid "jitted version of ``f``"
#~ msgstr ""

#~ msgid ""
#~ "Function that computes a (forward-mode)"
#~ " Jacobian-vector product of ``f``. "
#~ "Strictly speaking, this function is "
#~ "value_and_jvp."
#~ msgstr ""

#~ msgid "The function to compute jvp"
#~ msgstr ""

#~ msgid "input for ``f``"
#~ msgstr ""

#~ msgid "tangents"
#~ msgstr ""

#~ msgid ""
#~ "(``f(*inputs)``, jvp_tensor), where jvp_tensor "
#~ "is the same shape as the output"
#~ " of ``f``"
#~ msgstr ""

#~ msgid "Return the kronecker product of two matrices ``a`` and ``b``."
#~ msgstr ""

#~ msgid "kronecker product of ``a`` and ``b``"
#~ msgstr ""

#~ msgid "Return the maximum of an array or maximum along an axis."
#~ msgstr ""

#~ msgid "[description], defaults to None"
#~ msgstr ""

#~ msgid "Return the minimum of an array or minimum along an axis."
#~ msgstr ""

#~ msgid ""
#~ "Return the numpy array of a tensor"
#~ " ``a``, but may not work in a"
#~ " jitted function."
#~ msgstr ""

#~ msgid "numpy array of ``a``"
#~ msgstr ""

#~ msgid ""
#~ "One-hot encodes the given ``a``. "
#~ "Each index in the input ``a`` is"
#~ " encoded as a vector of zeros "
#~ "of length ``num`` with the element "
#~ "at index set to one:"
#~ msgstr ""

#~ msgid "input tensor"
#~ msgstr ""

#~ msgid "number of features in onehot dimension"
#~ msgstr ""

#~ msgid "onehot tensor with the last extra dimension"
#~ msgstr ""

#~ msgid ""
#~ "Return an ones-matrix of dimension "
#~ "`dim` Depending on specific backends, "
#~ "`dim` has to be either an int "
#~ "(numpy, torch, tensorflow) or a "
#~ "`ShapeType` object (for block-sparse "
#~ "backends). Block-sparse behavior is "
#~ "currently not supported Args:"
#~ msgstr ""

#~ msgid ""
#~ "shape (int): The dimension of the "
#~ "returned matrix. dtype: The dtype of "
#~ "the returned matrix."
#~ msgstr ""

#~ msgid ""
#~ "A jax like split API, but it "
#~ "doesn't split the key generator for "
#~ "other backends. It is just for a"
#~ " consistent interface of random code; "
#~ "make sure you know what the "
#~ "function actually does. This function is"
#~ " mainly a utility to write backend"
#~ " agnostic code instead of doing magic"
#~ " things."
#~ msgstr ""

#~ msgid "Return the elementwise real value of a tensor ``a``."
#~ msgstr ""

#~ msgid "real value of ``a``"
#~ msgstr ""

#~ msgid ""
#~ "Rectified linear unit activation function. "
#~ "Computes the element-wise function:"
#~ msgstr ""

#~ msgid "\\mathrm{relu}(x)=\\max(x,0)"
#~ msgstr ""

#~ msgid "Input tensor"
#~ msgstr ""

#~ msgid "Tensor after relu"
#~ msgstr ""

#~ msgid ""
#~ "Roughly equivalent to operand[indices] = "
#~ "updates, indices only support shape with"
#~ " rank 2 for now."
#~ msgstr ""

#~ msgid "Set the random state attached to the backend."
#~ msgstr ""

#~ msgid "the random seed, defaults to be None"
#~ msgstr ""

#~ msgid ""
#~ "If set to be true, only get "
#~ "the random state in return instead "
#~ "of setting the state on the "
#~ "backend"
#~ msgstr ""

#~ msgid "Return the  elementwise sine of a tensor ``a``."
#~ msgstr ""

#~ msgid "sine of ``a``"
#~ msgstr ""

#~ msgid "Return the total number of elements in ``a`` in tensor form."
#~ msgstr ""

#~ msgid "the total number of elements in ``a``"
#~ msgstr ""

#~ msgid ""
#~ "Softmax function. Computes the function "
#~ "which rescales elements to the range "
#~ "[0,1] such that the elements along "
#~ "axis sum to 1."
#~ msgstr ""

#~ msgid "\\mathrm{softmax}(x) = \\frac{\\exp(x_i)}{\\sum_j \\exp(x_j)}"
#~ msgstr ""

#~ msgid "Tensor"
#~ msgstr ""

#~ msgid ""
#~ "A dimension along which Softmax will "
#~ "be computed , defaults to None for"
#~ " all axis sum."
#~ msgstr ""

#~ msgid "concatenated tensor"
#~ msgstr ""

#~ msgid "Solve the linear system Ax=b and return the solution x."
#~ msgstr ""

#~ msgid "The multiplied matrix."
#~ msgstr ""

#~ msgid "The resulted matrix."
#~ msgstr ""

#~ msgid "The solution of the linear system."
#~ msgstr ""

#~ msgid "A sparse matrix multiplies a dense matrix."
#~ msgstr ""

#~ msgid "a sparse matrix"
#~ msgstr ""

#~ msgid "a dense matrix"
#~ msgstr ""

#~ msgid "dense matrix"
#~ msgstr ""

#~ msgid ""
#~ "Concatenates a sequence of tensors ``a``"
#~ " along a new dimension ``axis``."
#~ msgstr ""

#~ msgid "List of tensors in the same shape"
#~ msgstr ""

#~ msgid "the stack axis, defaults to 0"
#~ msgstr ""

#~ msgid "stateful register for each package"
#~ msgstr ""

#~ msgid "shape of output sampling tensor"
#~ msgstr ""

#~ msgid "only real data type is supported, \"32\" or \"64\", defaults to \"32\""
#~ msgstr ""

#~ msgid "Uniform random sampler from ``low`` to ``high``."
#~ msgstr ""

#~ msgid "shape of output sampling tensor, defaults to 1"
#~ msgstr ""

#~ msgid "Stop backpropagation from ``a``."
#~ msgstr ""

#~ msgid "``branches[index]()``"
#~ msgstr ""

#~ msgid "Constructs a tensor by tiling a given tensor."
#~ msgstr ""

#~ msgid "1d tensor with length the same as the rank of ``a``"
#~ msgstr ""

#~ msgid "Convert a sparse matrix to dense tensor."
#~ msgstr ""

#~ msgid "the resulted dense matrix"
#~ msgstr ""

#~ msgid ""
#~ "Find the unique elements and their "
#~ "corresponding counts of the given tensor"
#~ " ``a``."
#~ msgstr ""

#~ msgid "Unique elements, corresponding counts"
#~ msgstr ""

#~ msgid "Return the function which returns the value and grad of ``f``."
#~ msgstr ""

#~ msgid ""
#~ "the value and grad function of "
#~ "``f`` with the same set of "
#~ "arguments as ``f``"
#~ msgstr ""

#~ msgid ""
#~ "Return the VVAG function of ``f``. "
#~ "The inputs for ``f`` is (args[0], "
#~ "args[1], args[2], ...), and the output"
#~ " of ``f`` is a scalar. Suppose "
#~ "VVAG(f) is a function with inputs "
#~ "in the form (vargs[0], args[1], args[2],"
#~ " ...), where vagrs[0] has one extra"
#~ " dimension than args[0] in the first"
#~ " axis and consistent with args[0] in"
#~ " shape for remaining dimensions, i.e. "
#~ "shape(vargs[0]) = [batch] + shape(args[0])."
#~ " (We only cover cases where "
#~ "``vectorized_argnums`` defaults to 0 here "
#~ "for demonstration). VVAG(f) returns a "
#~ "tuple as a value tensor with shape"
#~ " [batch, 1] and a gradient tuple "
#~ "with shape: ([batch]+shape(args[argnum]) for "
#~ "argnum in argnums). The gradient for "
#~ "argnums=k is defined as"
#~ msgstr ""

#~ msgid ""
#~ "g^k = \\frac{\\partial \\sum_{i\\in batch} "
#~ "f(vargs[0][i], args[1], ...)}{\\partial args[k]}"
#~ msgstr ""

#~ msgid "Therefore, if argnums=0, the gradient is reduced to"
#~ msgstr ""

#~ msgid "g^0_i = \\frac{\\partial f(vargs[0][i])}{\\partial vargs[0][i]}"
#~ msgstr ""

#~ msgid ""
#~ ", which is specifically suitable for "
#~ "batched VQE optimization, where args[0] "
#~ "is the circuit parameters."
#~ msgstr ""

#~ msgid "And if argnums=1, the gradient is like"
#~ msgstr ""

#~ msgid ""
#~ "g^1_i = \\frac{\\partial \\sum_j "
#~ "f(vargs[0][j], args[1])}{\\partial args[1][i]}\n"
#~ "\n"
#~ msgstr ""

#~ msgid ""
#~ ", which is suitable for quantum "
#~ "machine learning scenarios, where ``f`` "
#~ "is the loss function, args[0] "
#~ "corresponds to the input data and "
#~ "args[1] corresponds to the weights in"
#~ " the QML model."
#~ msgstr ""

#~ msgid ""
#~ "the args to be vectorized, these "
#~ "arguments should share the same batch"
#~ " shape in the fist dimension"
#~ msgstr ""

#~ msgid ""
#~ "Function that computes the dot product"
#~ " between a vector v and the "
#~ "Jacobian of the given function at "
#~ "the point given by the inputs. "
#~ "(reverse mode AD relevant) Strictly "
#~ "speaking, this function is value_and_vjp."
#~ msgstr ""

#~ msgid "the function to carry out vjp calculation"
#~ msgstr ""

#~ msgid ""
#~ "value vector or gradient from downstream"
#~ " in reverse mode AD the same "
#~ "shape as return of function ``f``"
#~ msgstr ""

#~ msgid ""
#~ "(``f(*inputs)``, vjp_tensor), where vjp_tensor "
#~ "is the same shape as inputs"
#~ msgstr ""

#~ msgid ""
#~ "Return the vectorized map or batched "
#~ "version of ``f`` on the first "
#~ "extra axis. The general interface "
#~ "supports ``f`` with multiple arguments "
#~ "and broadcast in the fist dimension."
#~ msgstr ""

#~ msgid "function to be broadcasted."
#~ msgstr ""

#~ msgid "vmap version of ``f``"
#~ msgstr ""

#~ msgid ""
#~ "Return a zeros-matrix of dimension "
#~ "`dim` Depending on specific backends, "
#~ "`dim` has to be either an int "
#~ "(numpy, torch, tensorflow) or a "
#~ "`ShapeType` object (for block-sparse "
#~ "backends)."
#~ msgstr ""

#~ msgid "Block-sparse behavior is currently not supported Args:"
#~ msgstr ""

#~ msgid "Backend magic inherited from tensornetwork: numpy backend"
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`tensornetwork.backends.numpy.numpy_backend.NumPyBackend`"
#~ msgstr ""

#~ msgid ""
#~ "see the original backend API at "
#~ "`numpy backend "
#~ "<https://github.com/google/TensorNetwork/blob/master/tensornetwork/backends/numpy/numpy_backend.py>`_"
#~ msgstr ""

#~ msgid "Backend magic inherited from tensornetwork: pytorch backend"
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend`"
#~ msgstr ""

#~ msgid ""
#~ "See the original backend API at "
#~ "``pytorch backend``. "
#~ "`<https://github.com/google/TensorNetwork/blob/master/tensornetwork/backends/pytorch/pytorch_backend.py>`_"
#~ msgstr ""

#~ msgid ""
#~ "Note the functionality provided by "
#~ "pytorch backend is incomplete, it "
#~ "currenly lacks native efficicent jit and"
#~ " vmap support."
#~ msgstr ""

#~ msgid "Backend magic inherited from tensornetwork: tensorflow backend"
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend`"
#~ msgstr ""

#~ msgid ""
#~ "See the original backend API at "
#~ "`'tensorflow backend''. "
#~ "<https://github.com/google/TensorNetwork/blob/master/tensornetwork/backends/tensorflow/tensorflow_backend.py>`_"
#~ msgstr ""

#~ msgid "Some common noise quantum channels."
#~ msgstr ""

#~ msgid ""
#~ "Return an amplitude damping channel. "
#~ "Notice: Amplitude damping corrspondings to "
#~ "p = 1."
#~ msgstr ""

#~ msgid ""
#~ "\\sqrt{p}\n"
#~ "\\begin{bmatrix}\n"
#~ "    1 & 0\\\\\n"
#~ "    0 & \\sqrt{1-\\gamma}\\\\\n"
#~ "\\end{bmatrix}\\qquad\n"
#~ "\\sqrt{p}\n"
#~ "\\begin{bmatrix}\n"
#~ "    0 & \\sqrt{\\gamma}\\\\\n"
#~ "    0 & 0\\\\\n"
#~ "\\end{bmatrix}\\qquad\n"
#~ "\\sqrt{1-p}\n"
#~ "\\begin{bmatrix}\n"
#~ "    \\sqrt{1-\\gamma} & 0\\\\\n"
#~ "    0 & 1\\\\\n"
#~ "\\end{bmatrix}\\qquad\n"
#~ "\\sqrt{1-p}\n"
#~ "\\begin{bmatrix}\n"
#~ "    0 & 0\\\\\n"
#~ "    \\sqrt{\\gamma} & 0\\\\\n"
#~ "\\end{bmatrix}\n"
#~ "\n"
#~ msgstr ""

#~ msgid "the damping parameter of amplitude (:math:`\\gamma`)"
#~ msgstr ""

#~ msgid ":math:`p`"
#~ msgstr ""

#~ msgid "An amplitude damping channel with given :math:`\\gamma` and :math:`p`"
#~ msgstr ""

#~ msgid "Return a Depolarizing Channel"
#~ msgstr ""

#~ msgid ""
#~ "\\sqrt{1-p_x-p_y-p_z}\n"
#~ "\\begin{bmatrix}\n"
#~ "    1 & 0\\\\\n"
#~ "    0 & 1\\\\\n"
#~ "\\end{bmatrix}\\qquad\n"
#~ "\\sqrt{p_x}\n"
#~ "\\begin{bmatrix}\n"
#~ "    0 & 1\\\\\n"
#~ "    1 & 0\\\\\n"
#~ "\\end{bmatrix}\\qquad\n"
#~ "\\sqrt{p_y}\n"
#~ "\\begin{bmatrix}\n"
#~ "    0 & -1j\\\\\n"
#~ "    1j & 0\\\\\n"
#~ "\\end{bmatrix}\\qquad\n"
#~ "\\sqrt{p_z}\n"
#~ "\\begin{bmatrix}\n"
#~ "    1 & 0\\\\\n"
#~ "    0 & -1\\\\\n"
#~ "\\end{bmatrix}\n"
#~ "\n"
#~ msgstr ""

#~ msgid ":math:`p_x`"
#~ msgstr ""

#~ msgid ":math:`p_y`"
#~ msgstr ""

#~ msgid ":math:`p_z`"
#~ msgstr ""

#~ msgid "Sequences of Gates"
#~ msgstr ""

#~ msgid "Convert Kraus operators to one Tensor (as one Super Gate)."
#~ msgstr ""

#~ msgid ""
#~ "\\sum_{k}^{} K_k \\otimes K_k^{\\dagger}\n"
#~ "\n"
#~ msgstr ""

#~ msgid "A sequence of Gate"
#~ msgstr ""

#~ msgid "The corresponding Tensor of the list of Kraus operators"
#~ msgstr ""

#~ msgid "Return a phase damping channel with given :math:`\\gamma`"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}\n"
#~ "    1 & 0\\\\\n"
#~ "    0 & \\sqrt{1-\\gamma}\\\\\n"
#~ "\\end{bmatrix}\\qquad\n"
#~ "\\begin{bmatrix}\n"
#~ "    0 & 0\\\\\n"
#~ "    0 & \\sqrt{\\gamma}\\\\\n"
#~ "\\end{bmatrix}\n"
#~ "\n"
#~ msgstr ""

#~ msgid "The damping parameter of phase (:math:`\\gamma`)"
#~ msgstr ""

#~ msgid "A phase damping channel with given :math:`\\gamma`"
#~ msgstr ""

#~ msgid "Reset channel"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}\n"
#~ "    1 & 0\\\\\n"
#~ "    0 & 0\\\\\n"
#~ "\\end{bmatrix}\\qquad\n"
#~ "\\begin{bmatrix}\n"
#~ "    0 & 1\\\\\n"
#~ "    0 & 0\\\\\n"
#~ "\\end{bmatrix}\n"
#~ "\n"
#~ msgstr ""

#~ msgid "Check identity of a single qubit Kraus operators."
#~ msgstr ""

#~ msgid "Examples:"
#~ msgstr ""

#~ msgid ""
#~ "\\sum_{k}^{} K_k^{\\dagger} K_k = I\n"
#~ "\n"
#~ msgstr ""

#~ msgid "List of Kraus operators."
#~ msgstr ""

#~ msgid "Quantum circuit: state simulator"
#~ msgstr ""

#~ msgid "``Circuit`` class. Simple usage demo below."
#~ msgstr ""

#~ msgid "Apply any gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Qubit number than the gate applies on."
#~ msgstr ""

#~ msgid "Parameters for the gate"
#~ msgstr ""

#~ msgid "Apply cnot gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j & 0.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 1.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 1.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 1.+0.j & 0.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Qubit number than the gate applies on. The matrix for the gate is"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    1.+0.j & 0.+0.j & "
#~ "0.+0.j & 0.+0.j\\\\    0.+0.j & 1.+0.j"
#~ " & 0.+0.j & 0.+0.j\\\\    0.+0.j &"
#~ " 0.+0.j & 0.+0.j & 1.+0.j\\\\    "
#~ "0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j"
#~ " \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply cr gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply crx gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply cry gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply crz gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply cy gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j & 0.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 1.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.-1.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+1.j & 0.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    1.+0.j & 0.+0.j & "
#~ "0.+0.j & 0.+0.j\\\\    0.+0.j & 1.+0.j"
#~ " & 0.+0.j & 0.+0.j\\\\    0.+0.j &"
#~ " 0.+0.j & 0.+0.j & 0.-1.j\\\\    "
#~ "0.+0.j & 0.+0.j & 0.+1.j & 0.+0.j"
#~ " \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply cz gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j & 0.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 1.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & -1.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    1.+0.j & 0.+0.j & "
#~ "0.+0.j & 0.+0.j\\\\    0.+0.j & 1.+0.j"
#~ " & 0.+0.j & 0.+0.j\\\\    0.+0.j &"
#~ " 0.+0.j & 1.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 0.+0.j & 0.+0.j & -1.+0.j"
#~ " \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply exp gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply exp1 gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply h gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    "
#~ "0.70710677+0.j & 0.70710677+0.j\\\\    "
#~ "0.70710677+0.j & -0.70710677+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    0.70710677+0.j & "
#~ "0.70710677+0.j\\\\    0.70710677+0.j & "
#~ "-0.70710677+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply i gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j\\\\    0.+0.j & 1.+0.j "
#~ "\\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    1.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 1.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply iswap gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j & 0.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 0.+0.j & 0.+1.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+1.j & 0.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 1.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    1.+0.j & 0.+0.j & "
#~ "0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j"
#~ " & 0.+1.j & 0.+0.j\\\\    0.+0.j &"
#~ " 0.+1.j & 0.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j"
#~ " \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply r gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply rx gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply ry gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply rz gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply s gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j\\\\    0.+0.j & 0.+1.j "
#~ "\\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    1.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 0.+1.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply sd gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j\\\\    0.+0.j & 0.-1.j "
#~ "\\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    1.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 0.-1.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply swap gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j & 0.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 0.+0.j & 1.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 1.+0.j & 0.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 1.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    1.+0.j & 0.+0.j & "
#~ "0.+0.j & 0.+0.j\\\\    0.+0.j & 0.+0.j"
#~ " & 1.+0.j & 0.+0.j\\\\    0.+0.j &"
#~ " 1.+0.j & 0.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j"
#~ " \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply t gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1. &"
#~ " +0.j & 0. & +0.j\\\\    0. &"
#~ " +0.j & 0.70710677+0.70710677j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    1. & +0.j & 0. "
#~ "& +0.j\\\\    0. & +0.j & "
#~ "0.70710677+0.70710677j \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply td gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1. &"
#~ " +0.j & 0. & +0.j\\\\    0. &"
#~ " +0.j & 0.70710677-0.70710677j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    1. & +0.j & 0. "
#~ "& +0.j\\\\    0. & +0.j & "
#~ "0.70710677-0.70710677j \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply toffoli gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j & 0.+0.j & 0.+0.j & "
#~ "0.+0.j & 0.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 1.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 0.+0.j\\\\    0.+0.j & "
#~ "0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j\\\\"
#~ "    0.+0.j & 0.+0.j & 0.+0.j & "
#~ "1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 0.+0.j & 1.+0.j & "
#~ "0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 1.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 1.+0.j\\\\    0.+0.j & "
#~ "0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j & 1.+0.j & 0.+0.j "
#~ "\\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    1.+0.j & 0.+0.j & "
#~ "0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j\\\\    0.+0.j &"
#~ " 1.+0.j & 0.+0.j & 0.+0.j & "
#~ "0.+0.j & 0.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 0.+0.j\\\\    0.+0.j & "
#~ "0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j\\\\"
#~ "    0.+0.j & 0.+0.j & 0.+0.j & "
#~ "0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 0.+0.j & 0.+0.j & "
#~ "1.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 0.+0.j & 0.+0.j & "
#~ "1.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j &"
#~ " 1.+0.j & 0.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply wroot gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    "
#~ "0.70710677+0.j & -0.5 & -0.5j\\\\    0.5"
#~ " & -0.5j & 0.70710677+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    0.70710677+0.j & -0.5 &"
#~ " -0.5j\\\\    0.5 & -0.5j & "
#~ "0.70710677+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply x gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    0.+0.j "
#~ "& 1.+0.j\\\\    1.+0.j & 0.+0.j "
#~ "\\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    0.+0.j & 1.+0.j\\\\    "
#~ "1.+0.j & 0.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply y gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    0.+0.j "
#~ "& 0.-1.j\\\\    0.+1.j & 0.+0.j "
#~ "\\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    0.+0.j & 0.-1.j\\\\    "
#~ "0.+1.j & 0.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply z gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j\\\\    0.+0.j & -1.+0.j "
#~ "\\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    1.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & -1.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Circuit object based on state simulator."
#~ msgstr ""

#~ msgid "The number of qubits in the circuit."
#~ msgstr ""

#~ msgid ""
#~ "If not None, the initial state of"
#~ " the circuit is taken as ``inputs``"
#~ " instead of :math:`\\vert 0\\rangle^n` "
#~ "qubits, defaults to None"
#~ msgstr ""

#~ msgid "(Nodes, dangling Edges) for a MPS like initial wavefunction"
#~ msgstr ""

#~ msgid ""
#~ "dict if two qubit gate is ready"
#~ " for split, including parameters for "
#~ "at least one of ``max_singular_values`` "
#~ "and ``max_truncation_err``."
#~ msgstr ""

#~ msgid ""
#~ "Monte Carlo trajectory simulation of "
#~ "general Kraus channel whose Kraus "
#~ "operators cannot be amplified to unitary"
#~ " operators. For unitary operators composed"
#~ " Kraus channel, :py:meth:`unitary_kraus` is "
#~ "much faster."
#~ msgstr ""

#~ msgid ""
#~ "This function is jittable in theory. "
#~ "But only jax+GPU combination is "
#~ "recommended for jit since the graph "
#~ "building time is too long for "
#~ "other backend options; though the "
#~ "running time of the function is "
#~ "very fast for every case."
#~ msgstr ""

#~ msgid "list of ``tn.Node`` for Kraus operators"
#~ msgstr ""

#~ msgid "the qubits index that Kraus channel is applied on"
#~ msgstr ""

#~ msgid ""
#~ "random tensor between 0 or 1, "
#~ "defaults to be None, the random "
#~ "number will be generated automatically"
#~ msgstr ""

#~ msgid "Compute the expectation of corresponding operators."
#~ msgstr ""

#~ msgid ""
#~ "operator and its position on the "
#~ "circuit, eg. ``(tc.gates.z(), [1, ]), "
#~ "(tc.gates.x(), [2, ])`` is for operator"
#~ " :math:`Z_1X_2`"
#~ msgstr ""

#~ msgid ""
#~ "if True, then the wavefunction tensor"
#~ " is cached for further expectation "
#~ "evaluation, defaults to be true"
#~ msgstr ""

#~ msgid "Tensor with one element"
#~ msgstr ""

#~ msgid "[WIP], check whether the circuit is legal."
#~ msgstr ""

#~ msgid "the bool indicating whether the circuit is legal"
#~ msgstr ""

#~ msgid "Take measurement to the given quantum lines."
#~ msgstr ""

#~ msgid "measure on which quantum line"
#~ msgstr ""

#~ msgid "if true, theoretical probability is also returned"
#~ msgstr ""

#~ msgid ""
#~ "Middle measurement in z-basis on the "
#~ "circuit, note the wavefunction output is"
#~ " not normalized with ``mid_measurement`` "
#~ "involved, one should normalize the state"
#~ " manually if needed."
#~ msgstr ""

#~ msgid "the index of qubit that the Z direction postselection applied on"
#~ msgstr ""

#~ msgid "0 for spin up, 1 for spin down, defaults to be 0"
#~ msgstr ""

#~ msgid "Reference: arXiv:1201.3974."
#~ msgstr ""

#~ msgid "sampled bit string and the corresponding theoretical probability"
#~ msgstr ""

#~ msgid "Replace the input state with the circuit structure unchanged."
#~ msgstr ""

#~ msgid "Input wavefunction."
#~ msgstr ""

#~ msgid ""
#~ "Replace the input state in MPS "
#~ "representation while keep the circuit "
#~ "structure unchanged."
#~ msgstr ""

#~ msgid "Compute the output wavefunction from the circuit."
#~ msgstr ""

#~ msgid "the str indicating the form of the output wavefunction"
#~ msgstr ""

#~ msgid "Tensor with the corresponding shape"
#~ msgstr ""

#~ msgid "Compute :math:`\\langle bra\\vert ops \\vert ket\\rangle`"
#~ msgstr ""

#~ msgid "Example 1 (:math:`bra` is same as :math:`ket`)"
#~ msgstr ""

#~ msgid "Example 2 (:math:`bra` is different from :math:`ket`)"
#~ msgstr ""

#~ msgid "[description], defaults to None, which is the same as ``ket``"
#~ msgstr ""

#~ msgid "[description], defaults to True"
#~ msgstr ""

#~ msgid "[description], defaults to False"
#~ msgstr ""

#~ msgid ""
#~ "Not an ideal visualization for quantum"
#~ " circuit, but reserve here as a "
#~ "general approch to show tensornetwork "
#~ "[Deperacted, use ``qir2tex instead``]"
#~ msgstr ""

#~ msgid "Constants and setups"
#~ msgstr ""

#~ msgid ""
#~ "To set runtime contractor of the "
#~ "tensornetwork for a better contraction "
#~ "path."
#~ msgstr ""

#~ msgid ""
#~ "\"auto\", \"greedy\", \"branch\", \"plain\", "
#~ "\"tng\", \"custom\", \"custom_stateful\". defaults"
#~ " to None (\"auto\")"
#~ msgstr ""

#~ msgid "Valid for \"custom\" or \"custom_stateful\" as method, defaults to None"
#~ msgstr ""

#~ msgid ""
#~ "It is not very useful, as "
#~ "``memory_limit`` leads to ``branch`` "
#~ "contraction instead of ``greedy`` which "
#~ "is rather slow, defaults to None"
#~ msgstr ""

#~ msgid "Tensornetwork version is too low to support some of the contractors."
#~ msgstr ""

#~ msgid "Unknown method options."
#~ msgstr ""

#~ msgid "The new tensornetwork with its contractor set."
#~ msgstr ""

#~ msgid "To set the runtime numerical dtype of tensors."
#~ msgstr ""

#~ msgid ""
#~ "\"complex64\" or \"complex128\", defaults to"
#~ " None, which is equivalent to "
#~ "\"complex64\"."
#~ msgstr ""

#~ msgid "The naive state-vector simulator contraction path."
#~ msgstr ""

#~ msgid "The list of ``tn.Node``."
#~ msgstr ""

#~ msgid "The list of dangling node edges, defaults to be None."
#~ msgstr ""

#~ msgid "The ``tn.Node`` after contraction"
#~ msgstr ""

#~ msgid "To set the runtime backend of tensorcircuit."
#~ msgstr ""

#~ msgid ""
#~ "Note: ``tc.set_backend`` and "
#~ "``tc.cons.set_tensornetwork_backend`` are the same."
#~ msgstr ""

#~ msgid ""
#~ "\"numpy\", \"tensorflow\", \"jax\", \"pytorch\". "
#~ "defaults to None, which gives the "
#~ "same behavior as "
#~ "``tensornetwork.backend_contextmanager.get_default_backend()``."
#~ msgstr ""

#~ msgid "Whether the object should be set as global."
#~ msgstr ""

#~ msgid "Quantum circuit class but with density matrix simulator"
#~ msgstr ""

#~ msgid "Quantum circuit class but with density matrix simulator: v2"
#~ msgstr ""

#~ msgid "Bases: :py:class:`tensorcircuit.densitymatrix.DMCircuit`"
#~ msgstr ""

#~ msgid "Experimental features"
#~ msgstr ""

#~ msgid ""
#~ "Declarations of single-qubit and two-"
#~ "qubit gates and their corresponding "
#~ "matrix."
#~ msgstr ""

#~ msgid "Bases: :py:class:`tensornetwork.network_components.Node`"
#~ msgstr ""

#~ msgid "Wrapper of tn.Node, quantum gate"
#~ msgstr ""

#~ msgid "Bases: :py:class:`tensorcircuit.gates.GateF`"
#~ msgstr ""

#~ msgid "Note one should provide the gate with properly reshaped."
#~ msgstr ""

#~ msgid "corresponding gate"
#~ msgstr ""

#~ msgid "The name of the gate."
#~ msgstr ""

#~ msgid "the resulted gate"
#~ msgstr ""

#~ msgid "Convert the inputs to Tensor with specified dtype."
#~ msgstr ""

#~ msgid "inputs"
#~ msgstr ""

#~ msgid "dtype of the output Tensors"
#~ msgstr ""

#~ msgid "List of Tensors"
#~ msgstr ""

#~ msgid "Returns a LaTeX bmatrix."
#~ msgstr ""

#~ msgid "Formatted Display:"
#~ msgstr ""

#~ msgid ""
#~ "\\begin{bmatrix}    1.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 1.+0.j \\end{bmatrix}\n"
#~ "\n"
#~ msgstr ""

#~ msgid "2D numpy array"
#~ msgstr ""

#~ msgid "ValueError(\"bmatrix can at most display two dimensions\")"
#~ msgstr ""

#~ msgid "latex str for bmatrix of array a"
#~ msgstr ""

#~ msgid ""
#~ "Controlled rotation gate, when the "
#~ "control bit is 1, `rgate` is "
#~ "applied on the target gate."
#~ msgstr ""

#~ msgid "angle in radians"
#~ msgstr ""

#~ msgid "CR Gate"
#~ msgstr ""

#~ msgid ""
#~ "Faster exponential gate, directly implemented"
#~ " based on RHS, only work when: "
#~ ":math:`U^2` is identity matrix."
#~ msgstr ""

#~ msgid ""
#~ "\\rm{exp}(U) &= e^{-i \\theta U} \\\\\n"
#~ "        &= \\cos(\\theta) I - j \\sin(\\theta) U \\\\\n"
#~ "\n"
#~ msgstr ""

#~ msgid "input unitary (U)"
#~ msgstr ""

#~ msgid "suffix of Gate name"
#~ msgstr ""

#~ msgid "Exponential Gate"
#~ msgstr ""

#~ msgid "Exponential gate."
#~ msgstr ""

#~ msgid ""
#~ "\\rm{exp}(U) = e^{-i \\theta U}\n"
#~ "\n"
#~ msgstr ""

#~ msgid "iSwap gate."
#~ msgstr ""

#~ msgid ""
#~ "iSwap(\\theta) =\n"
#~ "\\begin{pmatrix}\n"
#~ "    1 & 0 & 0 & 0\\\\\n"
#~ "    0 & \\cos(\\frac{\\pi}{2} \\theta )"
#~ " & j \\sin(\\frac{\\pi}{2} \\theta ) "
#~ "& 0\\\\\n"
#~ "    0 & j \\sin(\\frac{\\pi}{2} \\theta"
#~ " ) & \\cos(\\frac{\\pi}{2} \\theta ) "
#~ "& 0\\\\\n"
#~ "    0 & 0 & 0 & 1\\\\\n"
#~ "\\end{pmatrix}\n"
#~ "\n"
#~ msgstr ""

#~ msgid "iSwap Gate"
#~ msgstr ""

#~ msgid "Convert Gate to numpy array."
#~ msgstr ""

#~ msgid "input Gate"
#~ msgstr ""

#~ msgid "corresponding Tensor"
#~ msgstr ""

#~ msgid ""
#~ "Inner helper function to generate gate"
#~ " functions, such as ``z()`` from "
#~ "``_z_matrix``"
#~ msgstr ""

#~ msgid "General single qubit rotation gate"
#~ msgstr ""

#~ msgid ""
#~ "R(\\theta, \\phi, \\alpha) = i \\cos(\\theta) I\n"
#~ "\n"
#~ msgstr ""

#~ msgid ""
#~ "- i \\cos(\\phi) \\sin(\\alpha) \\sin(\\theta) X\n"
#~ "\n"
#~ msgstr ""

#~ msgid ""
#~ "- i \\sin(\\phi) \\sin(\\alpha) \\sin(\\theta) Y\n"
#~ "\n"
#~ msgstr ""

#~ msgid ""
#~ "- i \\sin(\\theta) \\cos(\\alpha) Z\n"
#~ "\n"
#~ msgstr ""

#~ msgid "R Gate"
#~ msgstr ""

#~ msgid "Random single qubit gate described in https://arxiv.org/abs/2002.07730."
#~ msgstr ""

#~ msgid "A random single qubit gate"
#~ msgstr ""

#~ msgid "Returns a random two-qubit gate."
#~ msgstr ""

#~ msgid "a random two-qubit gate"
#~ msgstr ""

#~ msgid ""
#~ "Rotation gate, which is in matrix "
#~ "exponential form, shall give the same"
#~ " result as `rgate`."
#~ msgstr ""

#~ msgid ""
#~ "mx = \\sin(\\alpha) \\cos(\\phi) X\n"
#~ "\n"
#~ msgstr ""

#~ msgid ""
#~ "my = \\sin(\\alpha) \\sin(\\phi) Y\n"
#~ "\n"
#~ msgstr ""

#~ msgid ""
#~ "mz = \\cos(\\alpha) Z\n"
#~ "\n"
#~ msgstr ""

#~ msgid ""
#~ "R(\\theta, \\alpha, \\phi) = e^{-i\\theta (mx+my+mz)}\n"
#~ "\n"
#~ msgstr ""

#~ msgid "Rotation Gate"
#~ msgstr ""

#~ msgid "Rotation gate along X axis."
#~ msgstr ""

#~ msgid ""
#~ "RX(\\theta) = e^{-i\\frac{\\theta}{2}X}\n"
#~ "\n"
#~ msgstr ""

#~ msgid "RX Gate"
#~ msgstr ""

#~ msgid "Rotation gate along Y axis."
#~ msgstr ""

#~ msgid ""
#~ "RY(\\theta) = e^{-i\\frac{\\theta}{2}Y}\n"
#~ "\n"
#~ msgstr ""

#~ msgid "RY Gate"
#~ msgstr ""

#~ msgid "Rotation gate along Z axis."
#~ msgstr ""

#~ msgid ""
#~ "RZ(\\theta) = e^{-i\\frac{\\theta}{2}Z}\n"
#~ "\n"
#~ msgstr ""

#~ msgid "RZ Gate"
#~ msgstr ""

#~ msgid "Interfaces bridging different backends"
#~ msgstr ""

#~ msgid "Keras layer for tc quantum function"
#~ msgstr ""

#~ msgid ""
#~ "`QuantumLayer` wraps the quantum function "
#~ "`f` as a `keras.Layer` so that "
#~ "tensorcircuit is better integrated with "
#~ "tensorflow."
#~ msgstr ""

#~ msgid "[description], defaults to \"glorot_uniform\""
#~ msgstr ""

#~ msgid ""
#~ "Load function from the files in "
#~ "the ``tf.savedmodel`` format. We can "
#~ "load several functions at the same "
#~ "time, as they can be the same "
#~ "function of different input shapes."
#~ msgstr ""

#~ msgid ""
#~ "The fallback function when all functions"
#~ " loaded are failed, defaults to None"
#~ msgstr ""

#~ msgid ""
#~ "When there is not legal loaded "
#~ "function of the input shape and no"
#~ " fallback callable."
#~ msgstr ""

#~ msgid ""
#~ "A function that tries all loaded "
#~ "function against the input until the "
#~ "first success one."
#~ msgstr ""

#~ msgid ""
#~ "The keras loss function that directly"
#~ " taking the model output as the "
#~ "loss."
#~ msgstr ""

#~ msgid "Save tf function in the file (``tf.savedmodel`` format)."
#~ msgstr ""

#~ msgid "``tf.function`` ed function with graph building"
#~ msgstr ""

#~ msgid "the dir path to save the function"
#~ msgstr ""

#~ msgid "FiniteMPS from tensornetwork with bug fixed"
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`tensornetwork.matrixproductstates.finite_mps.FiniteMPS`"
#~ msgstr ""

#~ msgid ""
#~ "Apply a two-site gate to an "
#~ "MPS. This routine will in general "
#~ "destroy any canonical form of the "
#~ "state. If a canonical form is "
#~ "needed, the user can restore it "
#~ "using `FiniteMPS.position`."
#~ msgstr ""

#~ msgid "A two-body gate."
#~ msgstr ""

#~ msgid "The first site where the gate acts."
#~ msgstr ""

#~ msgid "The second site where the gate acts."
#~ msgstr ""

#~ msgid "The maximum number of singular values to keep."
#~ msgstr ""

#~ msgid "The maximum allowed truncation error."
#~ msgstr ""

#~ msgid ""
#~ "An optional value to choose the "
#~ "MPS tensor at `center_position` to be"
#~ " isometric after the application of "
#~ "the gate. Defaults to `site1`. If "
#~ "the MPS is canonical "
#~ "(i.e.`BaseMPS.center_position != None`), and "
#~ "if the orthogonality center coincides "
#~ "with either `site1` or `site2`,  the "
#~ "orthogonality center will be shifted to"
#~ " `center_position` (`site1` by default). If"
#~ " the orthogonality center does not "
#~ "coincide with `(site1, site2)` then "
#~ "`MPS.center_position` is set to `None`."
#~ msgstr ""

#~ msgid "Multiply `max_truncation_err` with the largest singular value."
#~ msgstr ""

#~ msgid ""
#~ "\"rank of gate is {} but has "
#~ "to be 4\", \"site1 = {} is "
#~ "not between 0 <= site < N -"
#~ " 1 = {}\", \"site2 = {} is "
#~ "not between 1 <= site < N ="
#~ " {}\",\"Found site2 ={}, site1={}. Only "
#~ "nearest neighbor gates are currently "
#~ "supported\", \"f center_position = "
#~ "{center_position} not  f in {(site1, "
#~ "site2)} \", or \"center_position = {},"
#~ " but gate is applied at sites "
#~ "{}, {}. Truncation should only be "
#~ "done if the gate is applied at "
#~ "the center position of the MPS.\""
#~ msgstr ""

#~ msgid "A scalar tensor containing the truncated weight of the truncation."
#~ msgstr ""

#~ msgid "Measure the expectation value of local operators `ops` site `sites`."
#~ msgstr ""

#~ msgid "A list Tensors of rank 2; the local operators to be measured."
#~ msgstr ""

#~ msgid "Sites where `ops` act."
#~ msgstr ""

#~ msgid "measurements :math:`\\langle` `ops[n]`:math:`\\rangle` for n in `sites`"
#~ msgstr ""

#~ msgid ""
#~ "Compute the correlator :math:`\\langle` "
#~ "`op1[site1], op2[s]`:math:`\\rangle` between `site1`"
#~ " and all sites `s` in `sites2`. "
#~ "If `s == site1`, `op2[s]` will be"
#~ " applied first."
#~ msgstr ""

#~ msgid "Tensor of rank 2; the local operator at `site1`."
#~ msgstr ""

#~ msgid "Tensor of rank 2; the local operator at `sites2`."
#~ msgstr ""

#~ msgid "The site where `op1`  acts"
#~ msgstr ""

#~ msgid "Sites where operator `op2` acts."
#~ msgstr ""

#~ msgid ""
#~ "Correlator :math:`\\langle` `op1[site1], "
#~ "op2[s]`:math:`\\rangle` for `s` :math:`\\in` "
#~ "`sites2`."
#~ msgstr ""

#~ msgid "Quantum circuit: MPS state simulator"
#~ msgstr ""

#~ msgid "``MPSCircuit`` class. Simple usage demo below."
#~ msgstr ""

#~ msgid "MPSCircuit object based on state simulator."
#~ msgstr ""

#~ msgid ""
#~ "If not None, the initial state of"
#~ " the circuit is taken as ``tensors``"
#~ " instead of :math:`\\vert 0\\rangle^n` "
#~ "qubits, defaults to None"
#~ msgstr ""

#~ msgid "The center position of MPS, default to 0"
#~ msgstr ""

#~ msgid "Apply a general qubit gate on MPS."
#~ msgstr ""

#~ msgid "The Gate to be applied"
#~ msgstr ""

#~ msgid "Qubit indices of the gate"
#~ msgstr ""

#~ msgid "\"MPS does not support application of gate on > 2 qubits.\""
#~ msgstr ""

#~ msgid ""
#~ "Apply a double qubit gate on "
#~ "adjacent qubits of Matrix Product States"
#~ " (MPS). Truncation rule is specified "
#~ "by `set_truncation_rule`."
#~ msgstr ""

#~ msgid "The first qubit index of the gate"
#~ msgstr ""

#~ msgid "The second qubit index of the gate"
#~ msgstr ""

#~ msgid "Center position of MPS, default is None"
#~ msgstr ""

#~ msgid ""
#~ "Apply a double qubit gate on MPS."
#~ " Truncation rule is specified by "
#~ "`set_truncation_rule`."
#~ msgstr ""

#~ msgid ""
#~ "Apply a single qubit gate on MPS,"
#~ " and the gate must be unitary; "
#~ "no truncation is needed."
#~ msgstr ""

#~ msgid "gate to be applied"
#~ msgstr ""

#~ msgid "Qubit index of the gate"
#~ msgstr ""

#~ msgid "Compute the conjugate of the current MPS."
#~ msgstr ""

#~ msgid "The constructed MPS"
#~ msgstr ""

#~ msgid "Copy the current MPS."
#~ msgstr ""

#~ msgid "Copy the current MPS without the tensors."
#~ msgstr ""

#~ msgid "Compute the expectation of the corresponding double qubit gate."
#~ msgstr ""

#~ msgid "qubit index of the gate"
#~ msgstr ""

#~ msgid ""
#~ "Compute the expectation of the "
#~ "corresponding single qubit gate in the"
#~ " form of tensor."
#~ msgstr ""

#~ msgid "Gate to be applied"
#~ msgstr ""

#~ msgid "The expectation of the corresponding single qubit gate"
#~ msgstr ""

#~ msgid ""
#~ "Compute the expectation of the direct"
#~ " product of the corresponding two "
#~ "gates."
#~ msgstr ""

#~ msgid "First gate to be applied"
#~ msgstr ""

#~ msgid "Second gate to be applied"
#~ msgstr ""

#~ msgid "Qubit index of the first gate"
#~ msgstr ""

#~ msgid "Qubit index of the second gate"
#~ msgstr ""

#~ msgid "The correlation of the corresponding two qubit gates"
#~ msgstr ""

#~ msgid "Construct the MPS from a given wavefunction."
#~ msgstr ""

#~ msgid "The given wavefunction (any shape is OK)"
#~ msgstr ""

#~ msgid ""
#~ "Compute the expectation of corresponding "
#~ "operators in the form of tensor."
#~ msgstr ""

#~ msgid ""
#~ "Operator and its position on the "
#~ "circuit, eg. ``(gates.Z(), [1]), (gates.X(),"
#~ " [2])`` is for operator :math:`Z_1X_2`"
#~ msgstr ""

#~ msgid "The expectation of corresponding operators"
#~ msgstr ""

#~ msgid "Get the normalized Center Position."
#~ msgstr ""

#~ msgid "Normalized Center Position."
#~ msgstr ""

#~ msgid "Check whether the circuit is legal."
#~ msgstr ""

#~ msgid "Whether the circuit is legal."
#~ msgstr ""

#~ msgid "integer indicating the measure on which quantum line"
#~ msgstr ""

#~ msgid ""
#~ "Middle measurement in the z-basis on "
#~ "the circuit, note the wavefunction "
#~ "output is not normalized with "
#~ "``mid_measurement`` involved, one should "
#~ "normalized the state manually if needed."
#~ msgstr ""

#~ msgid "The index of qubit that the Z direction postselection applied on"
#~ msgstr ""

#~ msgid "0 for spin up, 1 for spin down, defaults to 0"
#~ msgstr ""

#~ msgid "Normalize MPS Circuit according to the center position."
#~ msgstr ""

#~ msgid "Wrapper of tn.FiniteMPS.position. Set orthogonality center."
#~ msgstr ""

#~ msgid "The orthogonality center"
#~ msgstr ""

#~ msgid "Compute the projection between `other` as bra and `self` as ket."
#~ msgstr ""

#~ msgid "ket of the other MPS, which will be converted to bra automatically"
#~ msgstr ""

#~ msgid "The projection in form of tensor"
#~ msgstr ""

#~ msgid ""
#~ "Set truncation rules when double qubit"
#~ " gates are applied. If nothing is "
#~ "specified, no truncation will take place"
#~ " and the bond dimension will keep "
#~ "growing. For more details, refer to "
#~ "`split_tensor`."
#~ msgstr ""

#~ msgid "Tensor with shape [1, -1]"
#~ msgstr ""

#~ msgid ""
#~ "Split the tensor by SVD or QR "
#~ "depends on whether a truncation is "
#~ "required."
#~ msgstr ""

#~ msgid "The input tensor to split."
#~ msgstr ""

#~ msgid ""
#~ "Determine the orthogonal center is on"
#~ " the left tensor or the right "
#~ "tensor."
#~ msgstr ""

#~ msgid "Two tensors after splitting"
#~ msgstr ""

#~ msgid "Quantum state and operator class backend by tensornetwork"
#~ msgstr ""

#~ msgid "Bases: :py:class:`tensorcircuit.quantum.QuOperator`"
#~ msgstr ""

#~ msgid "Represents an adjoint (row) vector via a tensor network."
#~ msgstr ""

#~ msgid ""
#~ "Constructs a new `QuAdjointVector` from "
#~ "a tensor network. This encapsulates an"
#~ " existing tensor network, interpreting it"
#~ " as an adjoint vector (row vector)."
#~ msgstr ""

#~ msgid "The edges of the network to be used as the input edges."
#~ msgstr ""

#~ msgid ""
#~ "Nodes used to refer to parts of"
#~ " the tensor network that are not "
#~ "connected to any input or output "
#~ "edges (for example: a scalar factor)."
#~ msgstr ""

#~ msgid ""
#~ "Optional collection of edges to ignore"
#~ " when performing consistency checks."
#~ msgstr ""

#~ msgid ""
#~ "Construct a `QuAdjointVector` directly from"
#~ " a single tensor. This first wraps"
#~ " the tensor in a `Node`, then "
#~ "constructs the `QuAdjointVector` from that "
#~ "`Node`."
#~ msgstr ""

#~ msgid "The tensor for constructing an QuAdjointVector."
#~ msgstr ""

#~ msgid ""
#~ "Sequence of integer indices specifying "
#~ "the order in which to interpret "
#~ "the axes as subsystems (input edges)."
#~ " If not specified, the axes are "
#~ "taken in ascending order."
#~ msgstr ""

#~ msgid "The new constructed QuAdjointVector give from the given tensor."
#~ msgstr ""

#~ msgid ""
#~ "Represents a linear operator via a "
#~ "tensor network. To interpret a tensor"
#~ " network as a linear operator, some"
#~ " of the dangling edges must be "
#~ "designated as `out_edges` (output edges) "
#~ "and the rest as `in_edges` (input "
#~ "edges). Considered as a matrix, the "
#~ "`out_edges` represent the row index and"
#~ " the `in_edges` represent the column "
#~ "index. The (right) action of the "
#~ "operator on another then consists of "
#~ "connecting the `in_edges` of the first"
#~ " operator to the `out_edges` of the"
#~ " second. Can be used to do "
#~ "simple linear algebra with tensor "
#~ "networks."
#~ msgstr ""

#~ msgid ""
#~ "Creates a new `QuOperator` from a "
#~ "tensor network. This encapsulates an "
#~ "existing tensor network, interpreting it "
#~ "as a linear operator. The network "
#~ "is checked for consistency: All dangling"
#~ " edges must either be in `out_edges`,"
#~ " `in_edges`, or `ignore_edges`."
#~ msgstr ""

#~ msgid "The edges of the network to be used as the output edges."
#~ msgstr ""

#~ msgid ""
#~ "Optional collection of dangling edges to"
#~ " ignore when performing consistency checks."
#~ msgstr ""

#~ msgid ""
#~ "At least one reference node is "
#~ "required to specify a scalar. None "
#~ "provided!"
#~ msgstr ""

#~ msgid ""
#~ "The adjoint of the operator. This "
#~ "creates a new `QuOperator` with "
#~ "complex-conjugate copies of all tensors "
#~ "in the network and with the input"
#~ " and output edges switched."
#~ msgstr ""

#~ msgid ""
#~ "Check that the network has the "
#~ "expected dimensionality. This checks that "
#~ "all input and output edges are "
#~ "dangling and that there are no "
#~ "other dangling edges (except any "
#~ "specified in `ignore_edges`). If not, an"
#~ " exception is raised."
#~ msgstr ""

#~ msgid ""
#~ "Contract the tensor network in place."
#~ " This modifies the tensor network "
#~ "representation of the operator (or "
#~ "vector, or scalar), reducing it to "
#~ "a single tensor, without changing the"
#~ " value."
#~ msgstr ""

#~ msgid "Manually specify the axis ordering of the final tensor."
#~ msgstr ""

#~ msgid "The present object."
#~ msgstr ""

#~ msgid ""
#~ "Contracts the tensor network in place"
#~ " and returns the final tensor. Note"
#~ " that this modifies the tensor "
#~ "network representing the operator. The "
#~ "default ordering for the axes of "
#~ "the final tensor is: `*out_edges, "
#~ "*in_edges`. If there are any \"ignored\""
#~ " edges, their axes come first: "
#~ "`*ignored_edges, *out_edges, *in_edges`."
#~ msgstr ""

#~ msgid ""
#~ "Manually specify the axis ordering of"
#~ " the final tensor. The default "
#~ "ordering is determined by `out_edges` "
#~ "and `in_edges` (see above)."
#~ msgstr ""

#~ msgid "Node count '{}' > 1 after contraction!"
#~ msgstr ""

#~ msgid "The final tensor representing the operator."
#~ msgstr ""

#~ msgid ""
#~ "Construct a `QuOperator` directly from a"
#~ " single tensor. This first wraps the"
#~ " tensor in a `Node`, then constructs"
#~ " the `QuOperator` from that `Node`."
#~ msgstr ""

#~ msgid "The tensor."
#~ msgstr ""

#~ msgid "The axis indices of `tensor` to use as `out_edges`."
#~ msgstr ""

#~ msgid "The axis indices of `tensor` to use as `in_edges`."
#~ msgstr ""

#~ msgid "The new operator."
#~ msgstr ""

#~ msgid "All tensor-network nodes involved in the operator."
#~ msgstr ""

#~ msgid ""
#~ "The norm of the operator. This is"
#~ " the 2-norm (also known as the "
#~ "Frobenius or Hilbert-Schmidt norm)."
#~ msgstr ""

#~ msgid ""
#~ "The partial trace of the operator. "
#~ "Subsystems to trace out are supplied "
#~ "as indices, so that dangling edges "
#~ "are connected to each other as: "
#~ "`out_edges[i] ^ in_edges[i] for i in "
#~ "subsystems_to_trace_out` This does not modify"
#~ " the original network. The original "
#~ "ordering of the remaining subsystems is"
#~ " maintained."
#~ msgstr ""

#~ msgid "Indices of subsystems to trace out."
#~ msgstr ""

#~ msgid "A new QuOperator or QuScalar representing the result."
#~ msgstr ""

#~ msgid ""
#~ "Tensor product with another operator. "
#~ "Given two operators `A` and `B`, "
#~ "produces a new operator `AB` "
#~ "representing `A` ⊗ `B`. The `out_edges`"
#~ " (`in_edges`) of `AB` is simply the"
#~ " concatenation of the `out_edges` "
#~ "(`in_edges`) of `A.copy()` with that of"
#~ " `B.copy()`: `new_out_edges = [*out_edges_A_copy,"
#~ " *out_edges_B_copy]` `new_in_edges = "
#~ "[*in_edges_A_copy, *in_edges_B_copy]`"
#~ msgstr ""

#~ msgid "The other operator (`B`)."
#~ msgstr ""

#~ msgid "The result (`AB`)."
#~ msgstr ""

#~ msgid "The trace of the operator."
#~ msgstr ""

#~ msgid "Represents a scalar via a tensor network."
#~ msgstr ""

#~ msgid ""
#~ "Constructs a new `QuScalar` from a "
#~ "tensor network. This encapsulates an "
#~ "existing tensor network, interpreting it "
#~ "as a scalar."
#~ msgstr ""

#~ msgid ""
#~ "Nodes used to refer to the tensor"
#~ " network (need not be exhaustive -"
#~ " one node from each disconnected "
#~ "subnetwork is sufficient)."
#~ msgstr ""

#~ msgid ""
#~ "Construct a `QuScalar` directly from a"
#~ " single tensor. This first wraps the"
#~ " tensor in a `Node`, then constructs"
#~ " the `QuScalar` from that `Node`."
#~ msgstr ""

#~ msgid "The tensor for constructing a new QuScalar."
#~ msgstr ""

#~ msgid "The new constructed QuScalar from the given tensor."
#~ msgstr ""

#~ msgid "Represents a (column) vector via a tensor network."
#~ msgstr ""

#~ msgid ""
#~ "Constructs a new `QuVector` from a "
#~ "tensor network. This encapsulates an "
#~ "existing tensor network, interpreting it "
#~ "as a (column) vector."
#~ msgstr ""

#~ msgid ""
#~ "Construct a `QuVector` directly from a"
#~ " single tensor. This first wraps the"
#~ " tensor in a `Node`, then constructs"
#~ " the `QuVector` from that `Node`."
#~ msgstr ""

#~ msgid "The tensor for constructing a \"QuVector\"."
#~ msgstr ""

#~ msgid ""
#~ "Sequence of integer indices specifying "
#~ "the order in which to interpret "
#~ "the axes as subsystems (output edges)."
#~ " If not specified, the axes are "
#~ "taken in ascending order."
#~ msgstr ""

#~ msgid "The new constructed QuVector from the given tensor."
#~ msgstr ""

#~ msgid ""
#~ "Check the vector spaces represented by"
#~ " two lists of edges are compatible."
#~ " The number of edges must be "
#~ "the same and the dimensions of "
#~ "each pair of edges must match. "
#~ "Otherwise, an exception is raised. "
#~ ":param edges_1: List of edges "
#~ "representing a many-body Hilbert space."
#~ " :type edges_1: Sequence[Edge] :param "
#~ "edges_2: List of edges representing a"
#~ " many-body Hilbert space. :type "
#~ "edges_2: Sequence[Edge]"
#~ msgstr ""

#~ msgid ""
#~ "Hilbert-space mismatch: \"Cannot connect "
#~ "{} subsystems with {} subsystems\", or"
#~ " \"Input dimension {} != output "
#~ "dimension {}.\""
#~ msgstr ""

#~ msgid ""
#~ "Eliminates any connected CopyNodes that "
#~ "are identity matrices. This will modify"
#~ " the network represented by `nodes`. "
#~ "Only identities that are connected to"
#~ " other nodes are eliminated."
#~ msgstr ""

#~ msgid "Collection of nodes to search."
#~ msgstr ""

#~ msgid ""
#~ "The Dictionary mapping remaining Nodes "
#~ "to any replacements, Dictionary specifying "
#~ "all dangling-edge replacements."
#~ msgstr ""

#~ msgid "Compute the entropy from the given density matrix ``rho``."
#~ msgstr ""

#~ msgid "[description], defaults to 1e-12"
#~ msgstr ""

#~ msgid ""
#~ "Note: further jit is recommended. For"
#~ " large Hilbert space, sparse Hamiltonian"
#~ " is recommended"
#~ msgstr ""

#~ msgid ""
#~ "Construct a 'QuOperator' representing the "
#~ "identity on a given space. Internally,"
#~ " this is done by constructing "
#~ "'CopyNode's for each edge, with "
#~ "dimension according to 'space'."
#~ msgstr ""

#~ msgid ""
#~ "A sequence of integers for the "
#~ "dimensions of the tensor product factors"
#~ " of the space (the edges in the"
#~ " tensor network)."
#~ msgstr ""

#~ msgid "The data type (for conversion to dense)."
#~ msgstr ""

#~ msgid "The desired identity operator."
#~ msgstr ""

#~ msgid ""
#~ "Simulate the measuring of each qubit "
#~ "of ``p`` in the computational basis, "
#~ "thus producing output like that of "
#~ "``qiskit``."
#~ msgstr ""

#~ msgid ""
#~ "The quantum state, assumed to be "
#~ "normalized, as either a ket or "
#~ "density operator."
#~ msgstr ""

#~ msgid "The number of counts to perform."
#~ msgstr ""

#~ msgid ""
#~ "Defaults True. The bool indicating "
#~ "whether the return form is in the"
#~ " form of two array or one of"
#~ " the same length as the ``state`` "
#~ "(if ``sparse=False``)."
#~ msgstr ""

#~ msgid "The counts for each bit string measured."
#~ msgstr ""

#~ msgid ""
#~ "Constructs an appropriately specialized "
#~ "QuOperator. If there are no edges, "
#~ "creates a QuScalar. If the are "
#~ "only output (input) edges, creates a "
#~ "QuVector (QuAdjointVector). Otherwise creates "
#~ "a QuOperator."
#~ msgstr ""

#~ msgid ""
#~ "op = qu.quantum_constructor([], [psi_node[0], "
#~ "psi_node[1]]) >>> show_attributes(op) op.is_scalar()"
#~ "          -> False op.is_vector()          -> "
#~ "False op.is_adjoint_vector()  -> True "
#~ "len(op.out_edges)       -> 0 len(op.in_edges)"
#~ "        -> 2 >>> # psi_node[0] -> "
#~ "op.in_edges[0] >>> # psi_node[1] -> "
#~ "op.in_edges[1]"
#~ msgstr ""

#~ msgid "output edges."
#~ msgstr ""

#~ msgid "in edges."
#~ msgstr ""

#~ msgid ""
#~ "reference nodes for the tensor network"
#~ " (needed if there is a scalar "
#~ "component)."
#~ msgstr ""

#~ msgid "edges to ignore when checking the dimensionality of the tensor network."
#~ msgstr ""

#~ msgid "The new created QuOperator object."
#~ msgstr ""

#~ msgid "Compute the reduced density matrix from the quantum state ``state``."
#~ msgstr ""

#~ msgid "Compute the trace of several inputs ``o`` as tensor or ``QuOperator``."
#~ msgstr ""

#~ msgid "\\mathrm{Tr}(\\prod_i O_i)"
#~ msgstr ""

#~ msgid "the trace of several inputs"
#~ msgstr ""

#~ msgid "Tensornetwork Simplification"
#~ msgstr ""

#~ msgid ""
#~ "Get the new shape of two nodes,"
#~ " also supporting to return original "
#~ "shapes of two nodes."
#~ msgstr ""

#~ msgid "node one"
#~ msgstr ""

#~ msgid "node two"
#~ msgstr ""

#~ msgid "Whether to include original shape of two nodes, default is True."
#~ msgstr ""

#~ msgid "The new shape of the two nodes."
#~ msgstr ""

#~ msgid ""
#~ "Contract between Node ``a`` and ``b``,"
#~ " with correct shape only and no "
#~ "calculation"
#~ msgstr ""

#~ msgid "Shortcuts for measurement patterns on circuit"
#~ msgstr ""

#~ msgid "Some common graphs and lattices"
#~ msgstr ""

#~ msgid "1D chain with ``n`` sites"
#~ msgstr ""

#~ msgid ""
#~ "This measurements pattern is specifically "
#~ "suitable for vmap. Parameterize the "
#~ "Pauli string to be measured."
#~ msgstr ""

#~ msgid ""
#~ "parameter tensors determines what Pauli "
#~ "string to be measured, shape is "
#~ "[nwires, 4] if onehot is False."
#~ msgstr ""

#~ msgid ""
#~ "[description], defaults to False. If set"
#~ " to be True, structures will first"
#~ " go through onehot procedure."
#~ msgstr ""

#~ msgid "COO_sparse_matrix"
#~ msgstr ""

#~ msgid "a real and scalar tensor of shape []"
#~ msgstr ""

#~ msgid "Helper functions"
#~ msgstr ""

#~ msgid ""
#~ "Return a callable function for output"
#~ " ith parts of the original output "
#~ "along the first axis. Original output"
#~ " supports List and Tensor."
#~ msgstr ""

#~ msgid "The function to be applied this method"
#~ msgstr ""

#~ msgid "The ith parts of original output along the first axis (axis=0 or dim=0)"
#~ msgstr ""

#~ msgid "The modified callable function"
#~ msgstr ""

#~ msgid "Visualization on circuits"
#~ msgstr ""

#~ msgid "# TODO(@YHPeter): add examples"
#~ msgstr ""

#~ msgid ""
#~ "Generate the PDF file with given "
#~ "latex string and filename. Latex command"
#~ " and file path can be specified. "
#~ "When notebook is True, convert the "
#~ "output PDF file to image and "
#~ "return a Image object."
#~ msgstr ""

#~ msgid "String of latex content"
#~ msgstr ""

#~ msgid "File name, defaults to random UUID `str(uuid4())`"
#~ msgstr ""

#~ msgid "Executable Latex command, defaults to `pdflatex`"
#~ msgstr ""

#~ msgid "File path, defaults to current working place `os.getcwd()`"
#~ msgstr ""

#~ msgid "if notebook is True, return `Image` object; otherwise return `None`"
#~ msgstr ""

#~ msgid "_summary_"
#~ msgstr ""

#~ msgid "_description_"
#~ msgstr ""

#~ msgid "[description], default is None."
#~ msgstr ""

#~ msgid "_description_, default is (1, -1)."
#~ msgstr ""

#~ msgid "Apply fredkin gate on the circuit."
#~ msgstr ""

#~ msgid "Apply orx gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply ory gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply orz gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply ox gate on the circuit."
#~ msgstr ""

#~ msgid "Apply oy gate on the circuit."
#~ msgstr ""

#~ msgid "Apply oz gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Random tensor between 0 or 1, "
#~ "defaults to be None, the random "
#~ "number will be generated automatically"
#~ msgstr ""

#~ msgid "The str indicating the form of the output wavefunction."
#~ msgstr ""

#~ msgid ""
#~ "A collection of useful function snippets"
#~ " that irrelevant with the main "
#~ "modules or await for further refactor"
#~ msgstr ""

#~ msgid "VQNHE application"
#~ msgstr ""

#~ msgid "Apply **ANY** gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply **CR** gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply **CRX** gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply **CRY** gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply **CRZ** gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply **EXP** gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply **EXP1** gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply **FREDKIN** gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j & 0.+0.j & 0.+0.j & "
#~ "0.+0.j & 0.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 1.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 0.+0.j\\\\    0.+0.j & "
#~ "0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j\\\\"
#~ "    0.+0.j & 0.+0.j & 0.+0.j & "
#~ "1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 0.+0.j & 1.+0.j & "
#~ "0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 0.+0.j & 1.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 1.+0.j &"
#~ " 0.+0.j & 0.+0.j\\\\    0.+0.j & "
#~ "0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 1.+0.j "
#~ "\\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply **ISWAP** gate on the circuit."
#~ msgstr ""

#~ msgid "Apply **ORX** gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply **ORY** gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply **ORZ** gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply **OX** gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    0.+0.j "
#~ "& 1.+0.j & 0.+0.j & 0.+0.j\\\\    "
#~ "1.+0.j & 0.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 1.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply **OY** gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    0.+0.j "
#~ "& 0.-1.j & 0.+0.j & 0.+0.j\\\\    "
#~ "0.+1.j & 0.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 1.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply **OZ** gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number than the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j & 0.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & -1.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 1.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Apply **R** gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply **RX** gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply **RY** gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply **RZ** gate with parameters on the circuit."
#~ msgstr ""

#~ msgid "Apply **SD** gate on the circuit."
#~ msgstr ""

#~ msgid "Apply **TD** gate on the circuit."
#~ msgstr ""

#~ msgid "Apply **TOFFOLI** gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Apply unitary gates in ``kraus`` "
#~ "randomly based on corresponding ``prob``."
#~ msgstr ""

#~ msgid "Get the eigenvalues of matrix ``a``."
#~ msgstr ""

#~ msgid "eigenvalues of ``a``"
#~ msgstr ""

#~ msgid ""
#~ "Apply **ANY** gate with parameters on"
#~ " the circuit. See "
#~ ":py:meth:`tensorcircuit.gates.any_gate`."
#~ msgstr ""

#~ msgid "Qubit number that the gate applies on."
#~ msgstr ""

#~ msgid ""
#~ "Apply **CNOT** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.cnot_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j & 0.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 1.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 1.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 1.+0.j & 0.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid "Qubit number that the gate applies on. The matrix for the gate is"
#~ msgstr ""

#~ msgid ""
#~ "Apply **CR** gate with parameters on "
#~ "the circuit. See "
#~ ":py:meth:`tensorcircuit.gates.cr_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Apply **CRX** gate with parameters on"
#~ " the circuit. See "
#~ ":py:meth:`tensorcircuit.gates.crx_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Apply **CRY** gate with parameters on"
#~ " the circuit. See "
#~ ":py:meth:`tensorcircuit.gates.cry_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Apply **CRZ** gate with parameters on"
#~ " the circuit. See "
#~ ":py:meth:`tensorcircuit.gates.crz_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Apply **CY** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.cy_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j & 0.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 1.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.-1.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+1.j & 0.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **CZ** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.cz_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j & 0.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 1.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & -1.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **EXP** gate with parameters on"
#~ " the circuit. See "
#~ ":py:meth:`tensorcircuit.gates.exp_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Apply **EXP1** gate with parameters on"
#~ " the circuit. See "
#~ ":py:meth:`tensorcircuit.gates.exp1_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Apply **FREDKIN** gate on the circuit."
#~ " See :py:meth:`tensorcircuit.gates.fredkin_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j & 0.+0.j & 0.+0.j & "
#~ "0.+0.j & 0.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 1.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 0.+0.j\\\\    0.+0.j & "
#~ "0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j\\\\"
#~ "    0.+0.j & 0.+0.j & 0.+0.j & "
#~ "1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 0.+0.j & 1.+0.j & "
#~ "0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 0.+0.j & 1.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 1.+0.j &"
#~ " 0.+0.j & 0.+0.j\\\\    0.+0.j & "
#~ "0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 1.+0.j "
#~ "\\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **H** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.h_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    "
#~ "0.70710677+0.j & 0.70710677+0.j\\\\    "
#~ "0.70710677+0.j & -0.70710677+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **I** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.i_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j\\\\    0.+0.j & 1.+0.j "
#~ "\\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **ISWAP** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.iswap_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j & 0.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 0.+0.j & 0.+1.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+1.j & 0.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 1.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **ORX** gate with parameters on"
#~ " the circuit. See "
#~ ":py:meth:`tensorcircuit.gates.orx_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Apply **ORY** gate with parameters on"
#~ " the circuit. See "
#~ ":py:meth:`tensorcircuit.gates.ory_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Apply **ORZ** gate with parameters on"
#~ " the circuit. See "
#~ ":py:meth:`tensorcircuit.gates.orz_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Apply **OX** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.ox_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    0.+0.j "
#~ "& 1.+0.j & 0.+0.j & 0.+0.j\\\\    "
#~ "1.+0.j & 0.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 1.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **OY** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.oy_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    0.+0.j "
#~ "& 0.-1.j & 0.+0.j & 0.+0.j\\\\    "
#~ "0.+1.j & 0.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 1.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **OZ** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.oz_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j & 0.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & -1.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 1.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 1.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **R** gate with parameters on "
#~ "the circuit. See "
#~ ":py:meth:`tensorcircuit.gates.r_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Apply **RX** gate with parameters on "
#~ "the circuit. See "
#~ ":py:meth:`tensorcircuit.gates.rx_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Apply **RY** gate with parameters on "
#~ "the circuit. See "
#~ ":py:meth:`tensorcircuit.gates.ry_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Apply **RZ** gate with parameters on "
#~ "the circuit. See "
#~ ":py:meth:`tensorcircuit.gates.rz_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Apply **S** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.s_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j\\\\    0.+0.j & 0.+1.j "
#~ "\\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **SD** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.sd_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j\\\\    0.+0.j & 0.-1.j "
#~ "\\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **SWAP** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.swap_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j & 0.+0.j & 0.+0.j\\\\    "
#~ "0.+0.j & 0.+0.j & 1.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 1.+0.j & 0.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 1.+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **T** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.t_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1. &"
#~ " +0.j & 0. & +0.j\\\\    0. &"
#~ " +0.j & 0.70710677+0.70710677j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **TD** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.td_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1. &"
#~ " +0.j & 0. & +0.j\\\\    0. &"
#~ " +0.j & 0.70710677-0.70710677j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **TOFFOLI** gate on the circuit."
#~ " See :py:meth:`tensorcircuit.gates.toffoli_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j & 0.+0.j & 0.+0.j & "
#~ "0.+0.j & 0.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 1.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 0.+0.j\\\\    0.+0.j & "
#~ "0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j\\\\"
#~ "    0.+0.j & 0.+0.j & 0.+0.j & "
#~ "1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j\\\\    0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 0.+0.j & 1.+0.j & "
#~ "0.+0.j & 0.+0.j & 0.+0.j\\\\    0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 1.+0.j & 0.+0.j & "
#~ "0.+0.j\\\\    0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j & 0.+0.j & 0.+0.j &"
#~ " 0.+0.j & 1.+0.j\\\\    0.+0.j & "
#~ "0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j"
#~ " & 0.+0.j & 1.+0.j & 0.+0.j "
#~ "\\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **WROOT** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.wroot_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    "
#~ "0.70710677+0.j & -0.5 & -0.5j\\\\    0.5"
#~ " & -0.5j & 0.70710677+0.j \\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **X** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.x_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    0.+0.j "
#~ "& 1.+0.j\\\\    1.+0.j & 0.+0.j "
#~ "\\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **Y** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.y_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    0.+0.j "
#~ "& 0.-1.j\\\\    0.+1.j & 0.+0.j "
#~ "\\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply **Z** gate on the circuit. "
#~ "See :py:meth:`tensorcircuit.gates.z_gate`."
#~ msgstr ""

#~ msgid ""
#~ "Qubit number that the gate applies "
#~ "on. The matrix for the gate is"
#~ "  .. math::        \\begin{bmatrix}    1.+0.j "
#~ "& 0.+0.j\\\\    0.+0.j & -1.+0.j "
#~ "\\end{bmatrix}"
#~ msgstr ""

#~ msgid ""
#~ "Apply unitary gates in ``kraus`` "
#~ "randomly based on corresponding ``prob``. "
#~ "If ``prob`` is ``None``, this is "
#~ "reduced to kraus channel language."
#~ msgstr ""

#~ msgid "The density matrix simulator based on tensornetwork engine."
#~ msgstr ""

#~ msgid "Number of qubits"
#~ msgstr ""

#~ msgid "if True, nothing initialized, only for internal use, defaults to False"
#~ msgstr ""

#~ msgid "the state input for the circuit, defaults to None"
#~ msgstr ""

#~ msgid "the density matrix input for the circuit, defaults to None"
#~ msgstr ""

#~ msgid ""
#~ "Apply amplitudedamping quantum channel on "
#~ "the circuit. See "
#~ ":py:meth:`tensorcircuit.channels.amplitudedampingchannel`"
#~ msgstr ""

#~ msgid "Parameters for the channel."
#~ msgstr ""

#~ msgid "Return the output density matrix of the circuit."
#~ msgstr ""

#~ msgid ""
#~ "check whether the final return is "
#~ "a legal density matrix, defaults to "
#~ "False"
#~ msgstr ""

#~ msgid "whether to reuse previous results, defaults to True"
#~ msgstr ""

#~ msgid "The output densitymatrix in 2D shape tensor form"
#~ msgstr ""

#~ msgid ""
#~ "Apply depolarizing quantum channel on "
#~ "the circuit. See "
#~ ":py:meth:`tensorcircuit.channels.depolarizingchannel`"
#~ msgstr ""

#~ msgid ""
#~ "Apply phasedamping quantum channel on "
#~ "the circuit. See "
#~ ":py:meth:`tensorcircuit.channels.phasedampingchannel`"
#~ msgstr ""

#~ msgid ""
#~ "Apply reset quantum channel on the "
#~ "circuit. See "
#~ ":py:meth:`tensorcircuit.channels.resetchannel`"
#~ msgstr ""

#~ msgid "Generate tensorflow sparse matrix from Pauli string sum"
#~ msgstr ""

#~ msgid ""
#~ "1D Tensor representing for a Pauli "
#~ "string, e.g. [1, 0, 0, 3, 2] "
#~ "is for :math:`X_0Z_3Y_4`"
#~ msgstr ""

#~ msgid ""
#~ "the weight for the Pauli string "
#~ "defaults to None (all Pauli strings "
#~ "weight 1.0)"
#~ msgstr ""

#~ msgid "the tensorflow sparse matrix"
#~ msgstr ""

#~ msgid ""
#~ "2D Tensor, each row is for a "
#~ "Pauli string, e.g. [1, 0, 0, 3,"
#~ " 2] is for :math:`X_0Z_3Y_4`"
#~ msgstr ""

#~ msgid ""
#~ "1D Tensor, each element corresponds the"
#~ " weight for each Pauli string "
#~ "defaults to None (all Pauli strings "
#~ "weight 1.0)"
#~ msgstr ""

#~ msgid "the tensorflow coo sparse matrix"
#~ msgstr ""

#~ msgid "Generate scipy sparse matrix from Pauli string sum"
#~ msgstr ""

#~ msgid "the scipy coo sparse matrix"
#~ msgstr ""

#~ msgid "Generate tensorflow dense matrix from Pauli string sum"
#~ msgstr ""

#~ msgid "the tensorflow dense matrix"
#~ msgstr ""

#~ msgid ""
#~ "The projector of the operator. The "
#~ "operator, as a linear operator, on "
#~ "the adjoint of the operator."
#~ msgstr ""

#~ msgid ""
#~ "Set :math:`A` is the operator in "
#~ "matrix form, then the projector of "
#~ "operator is defined as: :math:`A^\\dagger "
#~ "A`"
#~ msgstr ""

#~ msgid "The projector of the operator."
#~ msgstr ""

#~ msgid "The reduced density of the operator."
#~ msgstr ""

#~ msgid ""
#~ "Set :math:`A` is the matrix of the"
#~ " operator, then the reduced density "
#~ "is defined as:"
#~ msgstr ""

#~ msgid "\\mathrm{Tr}_{subsystems}(A^\\dagger A)"
#~ msgstr ""

#~ msgid ""
#~ "Firstly, take the projector of the "
#~ "operator, then trace out the subsystems"
#~ " to trace out are supplied as "
#~ "indices, so that dangling edges are "
#~ "connected to each other as: "
#~ "`out_edges[i] ^ in_edges[i] for i in "
#~ "subsystems_to_trace_out` This does not modify"
#~ " the original network. The original "
#~ "ordering of the remaining subsystems is"
#~ " maintained."
#~ msgstr ""

#~ msgid ""
#~ "The QuOperator of the reduced density"
#~ " of the operator with given "
#~ "subsystems."
#~ msgstr ""

#~ msgid ""
#~ "Contracts the tensor network in place"
#~ " and returns the final tensor in "
#~ "two dimentional matrix. The default "
#~ "ordering for the axes of the final"
#~ " tensor is: (:math:`\\prod` dimension of"
#~ " out_edges, :math:`\\prod` dimension of "
#~ "in_edges)"
#~ msgstr ""

#~ msgid "The two-dimentional tensor representing the operator."
#~ msgstr ""

#~ msgid ""
#~ "Returns a bool indicating if QuOperator"
#~ " is an adjoint vector. Examples can"
#~ " be found in the `QuOperator.from_tensor`."
#~ msgstr ""

#~ msgid ""
#~ "Returns a bool indicating if QuOperator"
#~ " is a scalar. Examples can be "
#~ "found in the `QuOperator.from_tensor`."
#~ msgstr ""

#~ msgid ""
#~ "Returns a bool indicating if QuOperator"
#~ " is a vector. Examples can be "
#~ "found in the `QuOperator.from_tensor`."
#~ msgstr ""

#~ msgid ""
#~ "Tensor product with another operator. "
#~ "Given two operators `A` and `B`, "
#~ "produces a new operator `AB` "
#~ "representing :math:`A ⊗ B`. The "
#~ "`out_edges` (`in_edges`) of `AB` is "
#~ "simply the concatenation of the "
#~ "`out_edges` (`in_edges`) of `A.copy()` with"
#~ " that of `B.copy()`: `new_out_edges = "
#~ "[*out_edges_A_copy, *out_edges_B_copy]` `new_in_edges "
#~ "= [*in_edges_A_copy, *in_edges_B_copy]`"
#~ msgstr ""

#~ msgid ""
#~ "Set :math:`A` is the operator in "
#~ "matrix form, then the projector of "
#~ "operator is defined as: :math:`A "
#~ "A^\\dagger`"
#~ msgstr ""

#~ msgid "\\mathrm{Tr}_{subsystems}(A A^\\dagger)"
#~ msgstr ""

#~ msgid "Compute the double state of the given Hamiltonian operator ``h``."
#~ msgstr ""

#~ msgid "Hamiltonian operator in form of Tensor."
#~ msgstr ""

#~ msgid "The double state of ``h`` with the given ``beta``."
#~ msgstr ""

#~ msgid "Return fidelity scalar between two states rho and rho0."
#~ msgstr ""

#~ msgid "\\operatorname{Tr}(\\sqrt{\\sqrt{rho} rho_0 \\sqrt{rho}})"
#~ msgstr ""

#~ msgid "The density matrix in form of Tensor."
#~ msgstr ""

#~ msgid "The sqrtm of a Hermitian matrix ``a``."
#~ msgstr ""

#~ msgid "Compute the Gibbs state of the given Hamiltonian operator ``h``."
#~ msgstr ""

#~ msgid "The Gibbs state of ``h`` with the given ``beta``."
#~ msgstr ""

#~ msgid "Mutual information between AB subsystem described by ``cut``."
#~ msgstr ""

#~ msgid "The AB subsystem."
#~ msgstr ""

#~ msgid "The mutual information between AB subsystem described by ``cut``."
#~ msgstr ""

#~ msgid "Taylor expansion of :math:`ln(x+1)`."
#~ msgstr ""

#~ msgid "The :math:`k` th order, default is 2."
#~ msgstr ""

#~ msgid "The :math:`k` th order of Taylor expansion of :math:`ln(x+1)`."
#~ msgstr ""

#~ msgid ""
#~ "Compute the trace distance between two"
#~ " density matrix ``rho`` and ``rho2``."
#~ msgstr ""

#~ msgid "Epsilon, defaults to 1e-12"
#~ msgstr ""

#~ msgid "The trace distance between two density matrix ``rho`` and ``rho2``."
#~ msgstr ""

#~ msgid "\\operatorname{Tr}(\\prod_i O_i)"
#~ msgstr ""

#~ msgid ""
#~ "Compute the truncated free energy from"
#~ " the given density matrix ``rho``."
#~ msgstr ""

#~ msgid "The :math:`k` th order, defaults to 2"
#~ msgstr ""

#~ msgid "The :math:`k` th order of the truncated free energy."
#~ msgstr ""

#~ msgid ""
#~ "The circuit ansatz is firstly one "
#~ "layer of Hadamard gates and then "
#~ "we have ``nlayers`` blocks of "
#~ ":math:`e^{i\\theta Z_iZ_{i+1}}` two-qubit gate"
#~ " in ladder layout, following rx gate."
#~ msgstr ""

#~ msgid "The circuit"
#~ msgstr ""

#~ msgid "paramter tensor with 2*nlayer*n elements"
#~ msgstr ""

#~ msgid "number of ZZ+RX blocks, defaults to 2"
#~ msgstr ""

#~ msgid ""
#~ "whether use SVD split to reduce ZZ"
#~ " gate bond dimension, defaults to "
#~ "False"
#~ msgstr ""

#~ msgid "The circuit with example ansatz attached"
#~ msgstr ""

#~ msgid ""
#~ "Function decorator wraps the function "
#~ "with the first input and output in"
#~ " the format of circuit, the wrapped"
#~ " function has the first input and "
#~ "the output as the state tensor."
#~ msgstr ""

#~ msgid "Function with the fist input and the output as ``Circuit`` object."
#~ msgstr ""

#~ msgid ""
#~ "Wrapped function with the first input"
#~ " and the output as the state "
#~ "tensor correspondingly."
#~ msgstr ""

#~ msgid "Two-dimensional grid lattice"
#~ msgstr ""

#~ msgid "number of rows"
#~ msgstr ""

#~ msgid "number of cols"
#~ msgstr ""

#~ msgid "return all col edge with 1d index encoding"
#~ msgstr ""

#~ msgid ""
#~ "whether to include pbc edges (periodic"
#~ " boundary condition), defaults to False"
#~ msgstr ""

#~ msgid "list of col edge"
#~ msgstr ""

#~ msgid "return all row edge with 1d index encoding"
#~ msgstr ""

#~ msgid "list of row edge"
#~ msgstr ""

#~ msgid "Get the 2D grid lattice in ``nx.Graph`` format"
#~ msgstr ""

#~ msgid ""
#~ "whether to include pbc edges (periodic"
#~ " boundary condition), defaults to True"
#~ msgstr ""

#~ msgid ""
#~ "Generate a permutation matrix P. Due "
#~ "to the different convention or qubits'"
#~ " order in qiskit and tensorcircuit, "
#~ "the unitary represented by the same "
#~ "circuit is different. They are related"
#~ " by this permutation matrix P: P "
#~ "@ U_qiskit @ P = U_tc"
#~ msgstr ""

#~ msgid "# of qubits"
#~ msgstr ""

#~ msgid "The permutation matrix P"
#~ msgstr ""

#~ msgid ""
#~ "Generate a qiskit quantum circuit using"
#~ " the quantum intermediate representation "
#~ "(qir) in tensorcircuit."
#~ msgstr ""

#~ msgid "qiskit QuantumCircuit object"
#~ msgstr ""

#~ msgid ""
#~ "Generate a tensorcircuit circuit using "
#~ "the quantum circuit data in qiskit."
#~ msgstr ""

#~ msgid "Quantum circuit data from qiskit."
#~ msgstr ""

#~ msgid "Input state of the circuit. Default is None."
#~ msgstr ""

#~ msgid "A quantum circuit in tensorcircuit"
#~ msgstr ""

#~ msgid ""
#~ "Translating from the gate name to "
#~ "gate information including the number of"
#~ " control qubits and the reduced gate"
#~ " name."
#~ msgstr ""

#~ msgid "String of gate name"
#~ msgstr ""

#~ msgid "# of control qubits, reduced gate name"
#~ msgstr ""

#~ msgid ""
#~ "Generate Tex code from 'qir' string "
#~ "to illustrate the circuit structure. "
#~ "This visualization is based on quantikz"
#~ " package."
#~ msgstr ""

#~ msgid "The quantum intermediate representation of a circuit in tensorcircuit."
#~ msgstr ""

#~ msgid "Initial state, default is an all zero state '000...000'."
#~ msgstr ""

#~ msgid "Measurement Basis, default is None which means no"
#~ msgstr ""

#~ msgid ""
#~ "measurement in the end of the "
#~ "circuit. :type measure: Optional[List[str]] "
#~ ":param rcompress: If true, a right "
#~ "compression of the circuit will be "
#~ "conducted. A right compression means we"
#~ " will try to shift gates from "
#~ "right to left if possible. Default "
#~ "is false. :type rcompress: bool :param"
#~ " lcompress: If true, a left "
#~ "compression of the circuit will be "
#~ "conducted. A left compression means we"
#~ " will try to shift gates from "
#~ "left to right if possible. Default "
#~ "is false. :type lcompress: bool :param"
#~ " standalone: If true, the tex code"
#~ " will be designed to generate a "
#~ "standalone document. Default is false "
#~ "which means the generated tex code "
#~ "is just a quantikz code block. "
#~ ":type standalone: bool :param "
#~ "return_string_table: If true, a string "
#~ "table of tex code will also be "
#~ "returned. Default is false. :type "
#~ "return_string_table: bool :return: Tex code"
#~ " of circuit visualization based on "
#~ "quantikz package. If return_string_table is"
#~ " true, a string table of tex "
#~ "code will also be returned. :rtype: "
#~ "Union[str, Tuple[str, List[List[str]]]]"
#~ msgstr ""

#~ msgid ":math:`ket`."
#~ msgstr ""

#~ msgid ""
#~ "Get Pauli string array and weights "
#~ "array for a qubit Hamiltonian as a"
#~ " sum of Pauli strings defined in "
#~ "openfermion QubitOperator."
#~ msgstr ""

#~ msgid "Apply mpo gate in MPO format on the circuit."
#~ msgstr ""

#~ msgid "Apply multicontrol gate in MPO format on the circuit."
#~ msgstr ""

#~ msgid "Returns the amplitude of the circuit given the bitstring l."
#~ msgstr ""

#~ msgid "Apply the gate to two bits with given indexes."
#~ msgstr ""

#~ msgid "The Gate applied on bits."
#~ msgstr ""

#~ msgid "The index of the bit to apply the Gate."
#~ msgstr ""

#~ msgid "Apply the gate to the bit with the given index."
#~ msgstr ""

#~ msgid "The Gate applied on the bit."
#~ msgstr ""

#~ msgid ""
#~ "Return the list of nodes that "
#~ "consititues the expectation value just "
#~ "before the contraction."
#~ msgstr ""

#~ msgid "whether contract the output state firstly, defaults to True"
#~ msgstr ""

#~ msgid "The tensor network for the expectation"
#~ msgstr ""

#~ msgid ""
#~ "if true, we sample from the final"
#~ " state if memory allsows, True is "
#~ "prefered, defaults to False"
#~ msgstr ""

#~ msgid ""
#~ "List (if batch) of tuple (binary "
#~ "configuration tensor and correponding "
#~ "probability)"
#~ msgstr ""

#~ msgid "Sampling bistrings from the circuit output based on quantum amplitudes."
#~ msgstr ""

#~ msgid "tensorcircuit.densitymatrix2"
#~ msgstr ""

#~ msgid "Apply **CNOT** gate on the circuit."
#~ msgstr ""

#~ msgid "Apply **CY** gate on the circuit."
#~ msgstr ""

#~ msgid "Apply **CZ** gate on the circuit."
#~ msgstr ""

#~ msgid "Apply **H** gate on the circuit."
#~ msgstr ""

#~ msgid "Apply **I** gate on the circuit."
#~ msgstr ""

#~ msgid "Apply **S** gate on the circuit."
#~ msgstr ""

#~ msgid "Apply **SWAP** gate on the circuit."
#~ msgstr ""

#~ msgid "Apply **T** gate on the circuit."
#~ msgstr ""

#~ msgid "Apply **WROOT** gate on the circuit."
#~ msgstr ""

#~ msgid "Apply **X** gate on the circuit."
#~ msgstr ""

#~ msgid "Apply **Y** gate on the circuit."
#~ msgstr ""

#~ msgid "Apply **Z** gate on the circuit."
#~ msgstr ""

#~ msgid ""
#~ "Compute :math:`\\prod_{i\\in \\text{index}} s_i`,"
#~ " where the probability for each "
#~ "bitstring is given as a vector "
#~ "``results``."
#~ msgstr ""

#~ msgid "Generate sparse tensor from Pauli string sum"
#~ msgstr ""

#~ msgid "Generate dense matrix from Pauli string sum"
#~ msgstr ""

#~ msgid "Generate Heisenberg Hamiltonian with possible external fields."
#~ msgstr ""

#~ msgid "calibration qubit list"
#~ msgstr ""

#~ msgid "tensorcircuit.cloud"
#~ msgstr ""

#~ msgid "tensorcircuit.cloud.config"
#~ msgstr ""

#~ msgid "gate name list to be counted, defaults to None (counting all gates)"
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`tensornetwork.backends.jax.jax_backend.JaxBackend`, "
#~ ":py:class:`tensorcircuit.backends.abstract_backend.ExtendedBackend`"
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`tensornetwork.backends.numpy.numpy_backend.NumPyBackend`,"
#~ " "
#~ ":py:class:`tensorcircuit.backends.abstract_backend.ExtendedBackend`"
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend`,"
#~ " "
#~ ":py:class:`tensorcircuit.backends.abstract_backend.ExtendedBackend`"
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend`,"
#~ " "
#~ ":py:class:`tensorcircuit.backends.abstract_backend.ExtendedBackend`"
#~ msgstr ""

#~ msgid "Bases: :py:class:`tensorcircuit.abstractcircuit.AbstractCircuit`"
#~ msgstr ""

#~ msgid "Return a Depolarizing Channel for 1 qubit or 2 qubits"
#~ msgstr ""

#~ msgid "number of qubits, 1 and 2 are avaliable, defaults 1"
#~ msgstr ""

#~ msgid "Bases: :py:class:`tensorcircuit.basecircuit.BaseCircuit`"
#~ msgstr ""

#~ msgid "Bases: :py:class:`torch.nn.modules.module.Module`"
#~ msgstr ""

#~ msgid ""
#~ "This allows better BC support for "
#~ ":meth:`load_state_dict`. In :meth:`state_dict`, the"
#~ " version number will be saved as "
#~ "in the attribute `_metadata` of the "
#~ "returned state dict, and thus pickled."
#~ " `_metadata` is a dictionary with "
#~ "keys that follow the naming convention"
#~ " of state dict. See "
#~ "``_load_from_state_dict`` on how to use "
#~ "this information in loading."
#~ msgstr ""

#~ msgid ""
#~ "If new parameters/buffers are added/removed"
#~ " from a module, this number shall "
#~ "be bumped, and the module's "
#~ "`_load_from_state_dict` method can compare the"
#~ " version number and do appropriate "
#~ "changes if the state dict is from"
#~ " before the change."
#~ msgstr ""

#~ msgid ""
#~ "Note that extra state should be "
#~ "pickleable to ensure working serialization "
#~ "of the state_dict. We only provide "
#~ "provide backwards compatibility guarantees for"
#~ " serializing Tensors; other objects may "
#~ "break backwards compatibility if their "
#~ "serialized pickled form changes."
#~ msgstr ""

#~ msgid "*(string, torch.Tensor)* -- Tuple containing the name and buffer"
#~ msgstr ""

#~ msgid "*(string, Module)* -- Tuple containing a name and child module"
#~ msgstr ""

#~ msgid "*(string, Module)* -- Tuple of name and module"
#~ msgstr ""

#~ msgid "*(string, Parameter)* -- Tuple containing the name and parameter"
#~ msgstr ""

#~ msgid ""
#~ "The hook will be called every time"
#~ " after :func:`forward` has computed an "
#~ "output. It should have the following "
#~ "signature::"
#~ msgstr ""

#~ msgid ""
#~ "The input contains only the positional"
#~ " arguments given to the module. "
#~ "Keyword arguments won't be passed to "
#~ "the hooks and only to the "
#~ "``forward``. The hook can modify the "
#~ "output. It can modify the input "
#~ "inplace but it will not have "
#~ "effect on forward since this is "
#~ "called after :func:`forward` is called."
#~ msgstr ""

#~ msgid ""
#~ "The hook will be called every time"
#~ " before :func:`forward` is invoked. It "
#~ "should have the following signature::"
#~ msgstr ""

#~ msgid ""
#~ "The input contains only the positional"
#~ " arguments given to the module. "
#~ "Keyword arguments won't be passed to "
#~ "the hooks and only to the "
#~ "``forward``. The hook can modify the "
#~ "input. User can either return a "
#~ "tuple or a single modified value "
#~ "in the hook. We will wrap the "
#~ "value into a tuple if a single "
#~ "value is returned(unless that value is"
#~ " already a tuple)."
#~ msgstr ""

#~ msgid ""
#~ "The hook will be called every time"
#~ " the gradients with respect to module"
#~ " inputs are computed. The hook should"
#~ " have the following signature::"
#~ msgstr ""

#~ msgid "Returns a dictionary containing a whole state of the module."
#~ msgstr ""

#~ msgid ""
#~ "Visualise the circuit. This method "
#~ "recevies the keywords as same as "
#~ "qiskit.circuit.QuantumCircuit.draw. More details can"
#~ " be found here: "
#~ "https://qiskit.org/documentation/stubs/qiskit.circuit.QuantumCircuit.draw.html."
#~ msgstr ""

#~ msgid "the corresponding qubit"
#~ msgstr ""

#~ msgid "Bases: :py:class:`~keras.engine.training.Model`"
#~ msgstr ""

#~ msgid ""
#~ "This method can also be called "
#~ "directly on a Functional Model during"
#~ " construction. In this case, any loss"
#~ " Tensors passed to this Model must"
#~ " be symbolic and be able to be"
#~ " traced back to the model's `Input`s."
#~ " These losses become part of the "
#~ "model's topology and are tracked in "
#~ "`get_config`."
#~ msgstr ""

#~ msgid ""
#~ "Additional keyword arguments for backward "
#~ "compatibility. Accepted values:   inputs - "
#~ "Deprecated, will be automatically inferred."
#~ msgstr ""

#~ msgid ""
#~ "Additional keyword arguments for backward "
#~ "compatibility. Accepted values:"
#~ msgstr ""

#~ msgid "inputs - Deprecated, will be automatically inferred."
#~ msgstr ""

#~ msgid "Deprecated, will be automatically inferred."
#~ msgstr ""

#~ msgid "Whether to use `ResourceVariable`."
#~ msgstr ""

#~ msgid ""
#~ "When giving unsupported dtype and no "
#~ "initializer or when     trainable has "
#~ "been set to True with synchronization"
#~ " set as `ON_READ`."
#~ msgstr ""

#~ msgid "This is an alias of `self.__call__`."
#~ msgstr ""

#~ msgid "Input tensor(s)."
#~ msgstr ""

#~ msgid "additional positional arguments to be passed to `self.call`."
#~ msgstr ""

#~ msgid "additional keyword arguments to be passed to `self.call`."
#~ msgstr ""

#~ msgid "Output tensor(s)."
#~ msgstr ""

#~ msgid ""
#~ "1. In case of invalid user-"
#~ "provided data (not of type tuple,"
#~ "        list, `TensorShape`, or dict).     "
#~ "2. If the model requires call "
#~ "arguments that are agnostic        to "
#~ "the input shapes (positional or keyword"
#~ " arg in call signature).     3. If"
#~ " not all layers were properly built."
#~ "     4. If float type inputs are "
#~ "not supported within the layers."
#~ msgstr ""

#~ msgid ""
#~ "In case of invalid user-provided "
#~ "data (not of type tuple,        list,"
#~ " `TensorShape`, or dict).     2. If "
#~ "the model requires call arguments that"
#~ " are agnostic        to the input "
#~ "shapes (positional or keyword arg in "
#~ "call signature).     3. If not all "
#~ "layers were properly built.     4. If"
#~ " float type inputs are not supported"
#~ " within the layers."
#~ msgstr ""

#~ msgid ""
#~ "A mask or list of masks. A "
#~ "mask can be either a boolean "
#~ "tensor or None (no mask). For more"
#~ " details, check the guide   "
#~ "[here](https://www.tensorflow.org/guide/keras/masking_and_padding)."
#~ msgstr ""

#~ msgid ""
#~ "A mask or list of masks. A "
#~ "mask can be either a boolean "
#~ "tensor or None (no mask). For more"
#~ " details, check the guide"
#~ msgstr ""

#~ msgid ""
#~ "Loss function. Maybe be a string "
#~ "(name of loss function), or a "
#~ "`tf.keras.losses.Loss` instance. See "
#~ "`tf.keras.losses`. A loss function is "
#~ "any callable with the signature `loss"
#~ " = fn(y_true, y_pred)`, where `y_true` "
#~ "are the ground truth values, and "
#~ "`y_pred` are the model's predictions. "
#~ "`y_true` should have shape `(batch_size, "
#~ "d0, .. dN)` (except in the case"
#~ " of sparse loss functions such as "
#~ "sparse categorical crossentropy which expects"
#~ " integer arrays of shape `(batch_size, "
#~ "d0, .. dN-1)`). `y_pred` should have "
#~ "shape `(batch_size, d0, .. dN)`. The "
#~ "loss function should return a float "
#~ "tensor. If a custom `Loss` instance "
#~ "is used and reduction is set to"
#~ " `None`, return value has shape "
#~ "`(batch_size, d0, .. dN-1)` i.e. per-"
#~ "sample or per-timestep loss values; "
#~ "otherwise, it is a scalar. If the"
#~ " model has multiple outputs, you can"
#~ " use a different loss on each "
#~ "output by passing a dictionary or "
#~ "a list of losses. The loss value"
#~ " that will be minimized by the "
#~ "model will then be the sum of "
#~ "all individual losses, unless `loss_weights`"
#~ " is specified."
#~ msgstr ""

#~ msgid ""
#~ "List of metrics to be evaluated by"
#~ " the model during training and "
#~ "testing. Each of this can be a "
#~ "string (name of a built-in "
#~ "function), function or a "
#~ "`tf.keras.metrics.Metric` instance. See "
#~ "`tf.keras.metrics`. Typically you will use "
#~ "`metrics=['accuracy']`. A function is any "
#~ "callable with the signature `result ="
#~ " fn(y_true, y_pred)`. To specify different"
#~ " metrics for different outputs of a"
#~ " multi-output model, you could also"
#~ " pass a dictionary, such as "
#~ "`metrics={'output_a': 'accuracy', 'output_b': "
#~ "['accuracy', 'mse']}`. You can also pass"
#~ " a list to specify a metric or"
#~ " a list of metrics for each "
#~ "output, such as `metrics=[['accuracy'], "
#~ "['accuracy', 'mse']]` or `metrics=['accuracy', "
#~ "['accuracy', 'mse']]`. When you pass the"
#~ " strings 'accuracy' or 'acc', we "
#~ "convert this to one of "
#~ "`tf.keras.metrics.BinaryAccuracy`, "
#~ "`tf.keras.metrics.CategoricalAccuracy`, "
#~ "`tf.keras.metrics.SparseCategoricalAccuracy` based on "
#~ "the loss function used and the "
#~ "model output shape. We do a "
#~ "similar conversion for the strings "
#~ "'crossentropy' and 'ce' as well."
#~ msgstr ""

#~ msgid ""
#~ "Optional list or dictionary specifying "
#~ "scalar coefficients (Python floats) to "
#~ "weight the loss contributions of "
#~ "different model outputs. The loss value"
#~ " that will be minimized by the "
#~ "model will then be the *weighted "
#~ "sum* of all individual losses, weighted"
#~ " by the `loss_weights` coefficients.   If"
#~ " a list, it is expected to have"
#~ " a 1:1 mapping to the model's     "
#~ "outputs. If a dict, it is expected"
#~ " to map output names (strings)     to"
#~ " scalar coefficients."
#~ msgstr ""

#~ msgid ""
#~ "Optional list or dictionary specifying "
#~ "scalar coefficients (Python floats) to "
#~ "weight the loss contributions of "
#~ "different model outputs. The loss value"
#~ " that will be minimized by the "
#~ "model will then be the *weighted "
#~ "sum* of all individual losses, weighted"
#~ " by the `loss_weights` coefficients."
#~ msgstr ""

#~ msgid "If a list, it is expected to have a 1:1 mapping to the model's"
#~ msgstr ""

#~ msgid ""
#~ "outputs. If a dict, it is expected"
#~ " to map output names (strings) to "
#~ "scalar coefficients."
#~ msgstr ""

#~ msgid ""
#~ "Bool. Defaults to `False`. If `True`,"
#~ " this `Model`'s logic will not be "
#~ "wrapped in a `tf.function`. Recommended "
#~ "to leave this as `None` unless "
#~ "your `Model` cannot be run inside "
#~ "a `tf.function`. `run_eagerly=True` is not "
#~ "supported when using "
#~ "`tf.distribute.experimental.ParameterServerStrategy`."
#~ msgstr ""

#~ msgid ""
#~ "Int. Defaults to 1. The number of"
#~ " batches to run during each "
#~ "`tf.function` call. Running multiple batches"
#~ " inside a single `tf.function` call "
#~ "can greatly improve performance on TPUs"
#~ " or small models with a large "
#~ "Python overhead. At most, one full "
#~ "epoch will be run each execution. "
#~ "If a number larger than the size"
#~ " of the epoch is passed, the "
#~ "execution will be truncated to the "
#~ "size of the epoch. Note that if"
#~ " `steps_per_execution` is set to `N`, "
#~ "`Callback.on_batch_begin` and `Callback.on_batch_end` "
#~ "methods will only be called every "
#~ "`N` batches (i.e. before/after each "
#~ "`tf.function` execution)."
#~ msgstr ""

#~ msgid ""
#~ "If the layer has not been built,"
#~ " this method will call `build` on "
#~ "the layer. This assumes that the "
#~ "layer will later be used with "
#~ "inputs that match the input shape "
#~ "provided here."
#~ msgstr ""

#~ msgid ""
#~ "Shape tuple (tuple of integers) or "
#~ "list of shape tuples (one per "
#~ "output tensor of the layer). Shape "
#~ "tuples can include None for free "
#~ "dimensions, instead of an integer."
#~ msgstr ""

#~ msgid "An input shape tuple."
#~ msgstr ""

#~ msgid ""
#~ "Single TensorSpec or nested structure of"
#~ " TensorSpec objects, describing   how the"
#~ " layer would transform the provided "
#~ "input."
#~ msgstr ""

#~ msgid "Single TensorSpec or nested structure of TensorSpec objects, describing"
#~ msgstr ""

#~ msgid "how the layer would transform the provided input."
#~ msgstr ""

#~ msgid ""
#~ "Input data. It could be: - A "
#~ "Numpy array (or array-like), or a"
#~ " list of arrays   (in case the "
#~ "model has multiple inputs). - A "
#~ "TensorFlow tensor, or a list of "
#~ "tensors   (in case the model has "
#~ "multiple inputs). - A dict mapping "
#~ "input names to the corresponding "
#~ "array/tensors,   if the model has named"
#~ " inputs. - A `tf.data` dataset. "
#~ "Should return a tuple   of either "
#~ "`(inputs, targets)` or   `(inputs, targets,"
#~ " sample_weights)`. - A generator or "
#~ "`keras.utils.Sequence` returning `(inputs, targets)`"
#~ "   or `(inputs, targets, sample_weights)`. "
#~ "A more detailed description of unpacking"
#~ " behavior for iterator types (Dataset, "
#~ "generator, Sequence) is given in the "
#~ "`Unpacking behavior for iterator-like "
#~ "inputs` section of `Model.fit`."
#~ msgstr ""

#~ msgid "0 or 1. Verbosity mode. 0 = silent, 1 = progress bar."
#~ msgstr ""

#~ msgid ""
#~ "Optional Numpy array of weights for "
#~ "the test samples, used for weighting "
#~ "the loss function. You can either "
#~ "pass a flat (1D) Numpy array with"
#~ " the same length as the input "
#~ "samples   (1:1 mapping between weights "
#~ "and samples), or in the case of"
#~ "     temporal data, you can pass a"
#~ " 2D array with shape `(samples,     "
#~ "sequence_length)`, to apply a different "
#~ "weight to every timestep     of every"
#~ " sample. This argument is not "
#~ "supported when `x` is a     dataset, "
#~ "instead pass sample weights as the "
#~ "third element of `x`."
#~ msgstr ""

#~ msgid ""
#~ "List of `keras.callbacks.Callback` instances. "
#~ "List of callbacks to apply during "
#~ "evaluation. See "
#~ "[callbacks](/api_docs/python/tf/keras/callbacks)."
#~ msgstr ""

#~ msgid ""
#~ "`Model.evaluate` is not yet supported "
#~ "with `tf.distribute.experimental.ParameterServerStrategy`."
#~ msgstr ""

#~ msgid ""
#~ "Trains the model for a fixed "
#~ "number of epochs (iterations on a "
#~ "dataset)."
#~ msgstr ""

#~ msgid ""
#~ "Input data. It could be: - A "
#~ "Numpy array (or array-like), or a"
#~ " list of arrays   (in case the "
#~ "model has multiple inputs). - A "
#~ "TensorFlow tensor, or a list of "
#~ "tensors   (in case the model has "
#~ "multiple inputs). - A dict mapping "
#~ "input names to the corresponding "
#~ "array/tensors,   if the model has named"
#~ " inputs. - A `tf.data` dataset. "
#~ "Should return a tuple   of either "
#~ "`(inputs, targets)` or   `(inputs, targets,"
#~ " sample_weights)`. - A generator or "
#~ "`keras.utils.Sequence` returning `(inputs, targets)`"
#~ "   or `(inputs, targets, sample_weights)`. "
#~ "- A `tf.keras.utils.experimental.DatasetCreator`, "
#~ "which wraps a   callable that takes "
#~ "a single argument of type   "
#~ "`tf.distribute.InputContext`, and returns a "
#~ "`tf.data.Dataset`.   `DatasetCreator` should be "
#~ "used when users prefer to specify "
#~ "the   per-replica batching and sharding"
#~ " logic for the `Dataset`.   See "
#~ "`tf.keras.utils.experimental.DatasetCreator` doc for "
#~ "more   information. A more detailed "
#~ "description of unpacking behavior for "
#~ "iterator types (Dataset, generator, Sequence)"
#~ " is given below. If using "
#~ "`tf.distribute.experimental.ParameterServerStrategy`, only "
#~ "`DatasetCreator` type is supported for "
#~ "`x`."
#~ msgstr ""

#~ msgid ""
#~ "A more detailed description of unpacking"
#~ " behavior for iterator types (Dataset, "
#~ "generator, Sequence) is given below. If"
#~ " using `tf.distribute.experimental.ParameterServerStrategy`,"
#~ " only `DatasetCreator` type is supported"
#~ " for `x`."
#~ msgstr ""

#~ msgid ""
#~ "'auto', 0, 1, or 2. Verbosity "
#~ "mode. 0 = silent, 1 = progress "
#~ "bar, 2 = one line per epoch. "
#~ "'auto' defaults to 1 for most "
#~ "cases, but 2 when used with "
#~ "`ParameterServerStrategy`. Note that the "
#~ "progress bar is not particularly useful"
#~ " when logged to a file, so "
#~ "verbose=2 is recommended when not "
#~ "running interactively (eg, in a "
#~ "production environment)."
#~ msgstr ""

#~ msgid ""
#~ "Float between 0 and 1.  Fraction "
#~ "of the training data to be used"
#~ " as validation data.  The model will"
#~ " set apart this fraction of the "
#~ "training data,  will not train on "
#~ "it, and will evaluate  the loss "
#~ "and any model metrics  on this "
#~ "data at the end of each epoch."
#~ "  The validation data is selected "
#~ "from the last samples  in the `x`"
#~ " and `y` data provided, before "
#~ "shuffling. This argument is  not "
#~ "supported when `x` is a dataset, "
#~ "generator or `keras.utils.Sequence` instance.  "
#~ "`validation_split` is not yet supported "
#~ "with  `tf.distribute.experimental.ParameterServerStrategy`."
#~ msgstr ""

#~ msgid "Float between 0 and 1."
#~ msgstr ""

#~ msgid ""
#~ "Fraction of the training data to "
#~ "be used as validation data. The "
#~ "model will set apart this fraction "
#~ "of the training data, will not "
#~ "train on it, and will evaluate the"
#~ " loss and any model metrics on "
#~ "this data at the end of each "
#~ "epoch. The validation data is selected"
#~ " from the last samples in the "
#~ "`x` and `y` data provided, before "
#~ "shuffling. This argument is not "
#~ "supported when `x` is a dataset, "
#~ "generator or"
#~ msgstr ""

#~ msgid "`keras.utils.Sequence` instance."
#~ msgstr ""

#~ msgid ""
#~ "`validation_split` is not yet supported "
#~ "with `tf.distribute.experimental.ParameterServerStrategy`."
#~ msgstr ""

#~ msgid ""
#~ "Data on which to evaluate the loss"
#~ " and any model metrics at the "
#~ "end of each epoch. The model will"
#~ " not be trained on this data. "
#~ "Thus, note the fact that the "
#~ "validation loss of data provided using"
#~ " `validation_split` or `validation_data` is "
#~ "not affected by regularization layers "
#~ "like noise and dropout. `validation_data` "
#~ "will override `validation_split`. `validation_data`"
#~ " could be:   - A tuple `(x_val, "
#~ "y_val)` of Numpy arrays or tensors."
#~ "   - A tuple `(x_val, y_val, "
#~ "val_sample_weights)` of NumPy arrays.   - "
#~ "A `tf.data.Dataset`.   - A Python "
#~ "generator or `keras.utils.Sequence` returning   "
#~ "`(inputs, targets)` or `(inputs, targets, "
#~ "sample_weights)`. `validation_data` is not yet"
#~ " supported with "
#~ "`tf.distribute.experimental.ParameterServerStrategy`."
#~ msgstr ""

#~ msgid ""
#~ "Optional dictionary mapping class indices "
#~ "(integers) to a weight (float) value,"
#~ " used for weighting the loss function"
#~ " (during training only). This can be"
#~ " useful to tell the model to "
#~ "\"pay more attention\" to samples from"
#~ " an under-represented class."
#~ msgstr ""

#~ msgid ""
#~ "Optional Numpy array of weights for  "
#~ "the training samples, used for weighting"
#~ " the loss function  (during training "
#~ "only). You can either pass a flat"
#~ " (1D)  Numpy array with the same "
#~ "length as the input samples  (1:1 "
#~ "mapping between weights and samples),  "
#~ "or in the case of temporal data,"
#~ "  you can pass a 2D array with"
#~ " shape  `(samples, sequence_length)`,  to "
#~ "apply a different weight to every "
#~ "timestep of every sample. This  argument"
#~ " is not supported when `x` is a"
#~ " dataset, generator, or `keras.utils.Sequence`"
#~ " instance, instead provide the "
#~ "sample_weights  as the third element of"
#~ " `x`."
#~ msgstr ""

#~ msgid "Optional Numpy array of weights for"
#~ msgstr ""

#~ msgid ""
#~ "the training samples, used for weighting"
#~ " the loss function (during training "
#~ "only). You can either pass a flat"
#~ " (1D) Numpy array with the same "
#~ "length as the input samples (1:1 "
#~ "mapping between weights and samples), or"
#~ " in the case of temporal data, "
#~ "you can pass a 2D array with "
#~ "shape `(samples, sequence_length)`, to apply"
#~ " a different weight to every timestep"
#~ " of every sample. This argument is"
#~ " not supported when `x` is a "
#~ "dataset, generator, or"
#~ msgstr ""

#~ msgid "`keras.utils.Sequence` instance, instead provide the sample_weights"
#~ msgstr ""

#~ msgid "as the third element of `x`."
#~ msgstr ""

#~ msgid ""
#~ "Integer or `None`. Total number of "
#~ "steps (batches of samples) before "
#~ "declaring one epoch finished and "
#~ "starting the next epoch. When training"
#~ " with input tensors such as "
#~ "TensorFlow data tensors, the default "
#~ "`None` is equal to the number of"
#~ " samples in your dataset divided by"
#~ " the batch size, or 1 if that"
#~ " cannot be determined. If x is "
#~ "a `tf.data` dataset, and 'steps_per_epoch' "
#~ "is None, the epoch will run until"
#~ " the input dataset is exhausted. When"
#~ " passing an infinitely repeating dataset,"
#~ " you must specify the `steps_per_epoch` "
#~ "argument. If `steps_per_epoch=-1` the training"
#~ " will run indefinitely with an "
#~ "infinitely repeating dataset. This argument"
#~ " is not supported with array inputs."
#~ " When using "
#~ "`tf.distribute.experimental.ParameterServerStrategy`:   * "
#~ "`steps_per_epoch=None` is not supported."
#~ msgstr ""

#~ msgid ""
#~ "Integer or `None`. Total number of "
#~ "steps (batches of samples) before "
#~ "declaring one epoch finished and "
#~ "starting the next epoch. When training"
#~ " with input tensors such as "
#~ "TensorFlow data tensors, the default "
#~ "`None` is equal to the number of"
#~ " samples in your dataset divided by"
#~ " the batch size, or 1 if that"
#~ " cannot be determined. If x is "
#~ "a `tf.data` dataset, and 'steps_per_epoch' "
#~ "is None, the epoch will run until"
#~ " the input dataset is exhausted. When"
#~ " passing an infinitely repeating dataset,"
#~ " you must specify the `steps_per_epoch` "
#~ "argument. If `steps_per_epoch=-1` the training"
#~ " will run indefinitely with an "
#~ "infinitely repeating dataset. This argument"
#~ " is not supported with array inputs."
#~ " When using "
#~ "`tf.distribute.experimental.ParameterServerStrategy`:"
#~ msgstr ""

#~ msgid ""
#~ "Only relevant if validation data is "
#~ "provided. Integer or `collections.abc.Container` "
#~ "instance (e.g. list, tuple, etc.). If"
#~ " an integer, specifies how many "
#~ "training epochs to run before a "
#~ "new validation run is performed, e.g."
#~ " `validation_freq=2` runs validation every "
#~ "2 epochs. If a Container, specifies "
#~ "the epochs on which to run "
#~ "validation, e.g. `validation_freq=[1, 2, 10]`"
#~ " runs validation at the end of "
#~ "the 1st, 2nd, and 10th epochs."
#~ msgstr ""

#~ msgid ""
#~ "tf.keras.utils.Sequence to the `x` argument"
#~ " of fit, which will in fact "
#~ "yield not only features (x) but "
#~ "optionally targets (y) and sample "
#~ "weights. Keras requires that the output"
#~ " of such iterator-likes be "
#~ "unambiguous. The iterator should return "
#~ "a tuple of length 1, 2, or "
#~ "3, where the optional second and "
#~ "third elements will be used for y"
#~ " and sample_weight respectively. Any other"
#~ " type provided will be wrapped in "
#~ "a length one tuple, effectively treating"
#~ " everything as 'x'. When yielding "
#~ "dicts, they should still adhere to "
#~ "the top-level tuple structure. e.g. "
#~ "`({\"x0\": x0, \"x1\": x1}, y)`. Keras"
#~ " will not attempt to separate "
#~ "features, targets, and weights from the"
#~ " keys of a single dict."
#~ msgstr ""

#~ msgid "A notable unsupported data type is the namedtuple. The reason is that"
#~ msgstr ""

#~ msgid ""
#~ "it behaves like both an ordered "
#~ "datatype (tuple) and a mapping datatype"
#~ " (dict). So given a namedtuple of "
#~ "the form:"
#~ msgstr ""

#~ msgid "Retrieves losses relevant to a specific set of inputs."
#~ msgstr ""

#~ msgid "Input tensor or list/tuple of input tensors."
#~ msgstr ""

#~ msgid "List of loss tensors of the layer that depend on `inputs`."
#~ msgstr ""

#~ msgid "Retrieves updates relevant to a specific set of inputs."
#~ msgstr ""

#~ msgid "List of update ops of the layer that depend on `inputs`."
#~ msgstr ""

#~ msgid "Deprecated, do NOT use! Only for compatibility with external Keras."
#~ msgstr ""

#~ msgid ""
#~ "Loads all layer weights, either from "
#~ "a TensorFlow or an HDF5 weight "
#~ "file."
#~ msgstr ""

#~ msgid ""
#~ "If `by_name` is False weights are "
#~ "loaded based on the network's topology."
#~ " This means the architecture should "
#~ "be the same as when the weights"
#~ " were saved.  Note that layers that"
#~ " don't have weights are not taken "
#~ "into account in the topological "
#~ "ordering, so adding or removing layers"
#~ " is fine as long as they don't"
#~ " have weights."
#~ msgstr ""

#~ msgid ""
#~ "If `by_name` is True, weights are "
#~ "loaded into layers only if they "
#~ "share the same name. This is "
#~ "useful for fine-tuning or transfer-"
#~ "learning models where some of the "
#~ "layers have changed."
#~ msgstr ""

#~ msgid ""
#~ "Only topological loading (`by_name=False`) is"
#~ " supported when loading weights from "
#~ "the TensorFlow format. Note that "
#~ "topological loading differs slightly between"
#~ " TensorFlow and HDF5 formats for "
#~ "user-defined classes inheriting from "
#~ "`tf.keras.Model`: HDF5 loads based on a"
#~ " flattened list of weights, while the"
#~ " TensorFlow format loads based on the"
#~ " object-local names of attributes to"
#~ " which layers are assigned in the "
#~ "`Model`'s constructor."
#~ msgstr ""

#~ msgid ""
#~ "String, path to the weights file "
#~ "to load. For weight files in "
#~ "TensorFlow format, this is the file "
#~ "prefix (the same as was passed to"
#~ " `save_weights`). This can also be a"
#~ " path to a SavedModel saved from "
#~ "`model.save`."
#~ msgstr ""

#~ msgid ""
#~ "Boolean, whether to load weights by "
#~ "name or by topological order. Only "
#~ "topological loading is supported for "
#~ "weight files in TensorFlow format."
#~ msgstr ""

#~ msgid ""
#~ "Boolean, whether to skip loading of "
#~ "layers where there is a mismatch "
#~ "in the number of weights, or a "
#~ "mismatch in the shape of the "
#~ "weight (only valid when `by_name=True`)."
#~ msgstr ""

#~ msgid ""
#~ "Optional `tf.train.CheckpointOptions` object that"
#~ " specifies options for loading weights."
#~ msgstr ""

#~ msgid ""
#~ "When loading a weight file in "
#~ "TensorFlow format, returns the same "
#~ "status object as `tf.train.Checkpoint.restore`. "
#~ "When graph building, restore ops are "
#~ "run automatically as soon as the "
#~ "network is built (on first call "
#~ "for user-defined classes inheriting from"
#~ " `Model`, immediately if it is "
#~ "already built).  When loading weights in"
#~ " HDF5 format, returns `None`."
#~ msgstr ""

#~ msgid ""
#~ "When loading a weight file in "
#~ "TensorFlow format, returns the same "
#~ "status object as `tf.train.Checkpoint.restore`. "
#~ "When graph building, restore ops are "
#~ "run automatically as soon as the "
#~ "network is built (on first call "
#~ "for user-defined classes inheriting from"
#~ " `Model`, immediately if it is "
#~ "already built)."
#~ msgstr ""

#~ msgid "When loading weights in HDF5 format, returns `None`."
#~ msgstr ""

#~ msgid "If `h5py` is not available and the weight file is in HDF5     format."
#~ msgstr ""

#~ msgid "If `skip_mismatch` is set to `True` when `by_name` is     `False`."
#~ msgstr ""

#~ msgid ""
#~ "Returns the model's metrics added using"
#~ " `compile()`, `add_metric()` APIs."
#~ msgstr ""

#~ msgid ""
#~ "Computation is done in batches. This "
#~ "method is designed for performance in"
#~ " large scale inputs. For small amount"
#~ " of inputs that fit in one "
#~ "batch, directly using `__call__()` is "
#~ "recommended for faster execution, e.g., "
#~ "`model(x)`, or `model(x, training=False)` if"
#~ " you have layers such as "
#~ "`tf.keras.layers.BatchNormalization` that behaves "
#~ "differently during inference. Also, note "
#~ "the fact that test loss is not "
#~ "affected by regularization layers like "
#~ "noise and dropout."
#~ msgstr ""

#~ msgid "Verbosity mode, 0 or 1."
#~ msgstr ""

#~ msgid ""
#~ "List of `keras.callbacks.Callback` instances. "
#~ "List of callbacks to apply during "
#~ "prediction. See "
#~ "[callbacks](/api_docs/python/tf/keras/callbacks)."
#~ msgstr ""

#~ msgid "If `model.predict_on_batch` is wrapped in a `tf.function`."
#~ msgstr ""

#~ msgid ""
#~ "This method should contain the "
#~ "mathematical logic for one step of "
#~ "inference. This typically includes the "
#~ "forward pass."
#~ msgstr ""

#~ msgid "Saves the model to Tensorflow SavedModel or a single HDF5 file."
#~ msgstr ""

#~ msgid ""
#~ "Please see `tf.keras.models.save_model` or the"
#~ " [Serialization and Saving "
#~ "guide](https://keras.io/guides/serialization_and_saving/) for"
#~ " details."
#~ msgstr ""

#~ msgid "String, PathLike, path to SavedModel or H5 file to save the model."
#~ msgstr ""

#~ msgid "If True, save optimizer's state together."
#~ msgstr ""

#~ msgid ""
#~ "Either `'tf'` or `'h5'`, indicating "
#~ "whether to save the model to "
#~ "Tensorflow SavedModel or HDF5. Defaults "
#~ "to 'tf' in TF 2.X, and 'h5' "
#~ "in TF 1.X."
#~ msgstr ""

#~ msgid ""
#~ "Signatures to save with the SavedModel."
#~ " Applicable to the 'tf' format only."
#~ " Please see the `signatures` argument "
#~ "in `tf.saved_model.save` for details."
#~ msgstr ""

#~ msgid ""
#~ "(only applies to SavedModel format) "
#~ "`tf.saved_model.SaveOptions` object that specifies"
#~ " options for saving to SavedModel."
#~ msgstr ""

#~ msgid ""
#~ "(only applies to SavedModel format) When"
#~ " enabled, the SavedModel will store "
#~ "the function traces for each layer. "
#~ "This can be disabled, so that only"
#~ " the configs of each layer are "
#~ "stored. Defaults to `True`. Disabling "
#~ "this will decrease serialization time "
#~ "and reduce file size, but it "
#~ "requires that all custom layers/models "
#~ "implement a `get_config()` method."
#~ msgstr ""

#~ msgid "```python from keras.models import load_model"
#~ msgstr ""

#~ msgid ""
#~ "model.save('my_model.h5')  # creates a HDF5"
#~ " file 'my_model.h5' del model  # "
#~ "deletes the existing model"
#~ msgstr ""

#~ msgid ""
#~ "# returns a compiled model # "
#~ "identical to the previous one model "
#~ "= load_model('my_model.h5') ```"
#~ msgstr ""

#~ msgid "Returns the `tf.TensorSpec` of call inputs as a tuple `(args, kwargs)`."
#~ msgstr ""

#~ msgid ""
#~ "# arg_specs is `[tf.TensorSpec(...), ...]`."
#~ " kwarg_specs, in this example, is #"
#~ " an empty dict since functional "
#~ "models do not use keyword arguments. "
#~ "arg_specs, kwarg_specs = model.save_spec()"
#~ msgstr ""

#~ msgid ""
#~ "'serving_default': serve.get_concrete_function(*arg_specs, "
#~ "**kwarg_specs)"
#~ msgstr ""

#~ msgid ""
#~ "The TensorFlow format matches objects "
#~ "and variables by starting at a "
#~ "root object, `self` for `save_weights`, "
#~ "and greedily matching attribute names. "
#~ "For `Model.save` this is the `Model`,"
#~ " and for `Checkpoint.save` this is "
#~ "the `Checkpoint` even if the "
#~ "`Checkpoint` has a model attached. This"
#~ " means saving a `tf.keras.Model` using "
#~ "`save_weights` and loading into a "
#~ "`tf.train.Checkpoint` with a `Model` attached"
#~ " (or vice versa) will not match "
#~ "the `Model`'s variables. See the [guide"
#~ " to training "
#~ "checkpoints](https://www.tensorflow.org/guide/checkpoint) for"
#~ " details on the TensorFlow format."
#~ msgstr ""

#~ msgid ""
#~ "Either 'tf' or 'h5'. A `filepath` "
#~ "ending in '.h5' or '.keras' will "
#~ "default to HDF5 if `save_format` is "
#~ "`None`. Otherwise `None` defaults to "
#~ "'tf'."
#~ msgstr ""

#~ msgid "If `h5py` is not available when attempting to save in HDF5     format."
#~ msgstr ""

#~ msgid ""
#~ "Relative or absolute positions of log"
#~ " elements in each line. If not "
#~ "provided, defaults to `[.33, .55, .67,"
#~ " 1.]`."
#~ msgstr ""

#~ msgid ""
#~ "Print function to use. Defaults to "
#~ "`print`. It will be called on each"
#~ " line of the summary. You can "
#~ "set it to a custom function in "
#~ "order to capture the string summary."
#~ msgstr ""

#~ msgid ""
#~ "Whether to expand the nested models. "
#~ "If not provided, defaults to `False`."
#~ msgstr ""

#~ msgid ""
#~ "Input data. It could be: - A "
#~ "Numpy array (or array-like), or a"
#~ " list of arrays (in case the     "
#~ "model has multiple inputs). - A "
#~ "TensorFlow tensor, or a list of "
#~ "tensors (in case the model has     "
#~ "multiple inputs). - A dict mapping "
#~ "input names to the corresponding "
#~ "array/tensors, if     the model has "
#~ "named inputs."
#~ msgstr ""

#~ msgid "A dict mapping input names to the corresponding array/tensors, if"
#~ msgstr ""

#~ msgid "the model has named inputs."
#~ msgstr ""

#~ msgid "If `model.test_on_batch` is wrapped in a `tf.function`."
#~ msgstr ""

#~ msgid "Additional keyword arguments to be passed to `json.dumps()`."
#~ msgstr ""

#~ msgid ""
#~ "Optional dictionary mapping class indices "
#~ "(integers) to a weight (float) to "
#~ "apply to the model's loss for the"
#~ " samples from this class during "
#~ "training. This can be useful to "
#~ "tell the model to \"pay more "
#~ "attention\" to samples from an under-"
#~ "represented class."
#~ msgstr ""

#~ msgid ""
#~ "This method can be overridden to "
#~ "support custom training logic. For "
#~ "concrete examples of how to override "
#~ "this method see [Customizing what "
#~ "happends in "
#~ "fit](https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit)."
#~ " This method is called by "
#~ "`Model.make_train_function`."
#~ msgstr ""

#~ msgid ""
#~ "This method should contain the "
#~ "mathematical logic for one step of "
#~ "training. This typically includes the "
#~ "forward pass, loss calculation, "
#~ "backpropagation, and metric updates."
#~ msgstr ""

#~ msgid "Bases: :py:class:`~keras.engine.base_layer.Layer`"
#~ msgstr ""

#~ msgid ""
#~ "Input tensor, or dict/list/tuple of "
#~ "input tensors. The first positional "
#~ "`inputs` argument is subject to special"
#~ " rules: - `inputs` must be explicitly"
#~ " passed. A layer cannot have zero"
#~ "   arguments, and `inputs` cannot be "
#~ "provided via the default value   of "
#~ "a keyword argument. - NumPy array "
#~ "or Python scalar values in `inputs` "
#~ "get cast as tensors. - Keras mask"
#~ " metadata is only collected from "
#~ "`inputs`. - Layers are built "
#~ "(`build(input_shape)` method)   using shape "
#~ "info from `inputs` only. - `input_spec`"
#~ " compatibility is only checked against "
#~ "`inputs`. - Mixed precision input "
#~ "casting is only applied to `inputs`."
#~ "   If a layer has tensor arguments"
#~ " in `*args` or `**kwargs`, their   "
#~ "casting behavior in mixed precision "
#~ "should be handled manually. - The "
#~ "SavedModel input specification is generated"
#~ " using `inputs` only. - Integration "
#~ "with various ecosystem packages like "
#~ "TFMOT, TFLite,   TF.js, etc is only "
#~ "supported for `inputs` and not for "
#~ "tensors in   positional and keyword "
#~ "arguments."
#~ msgstr ""

#~ msgid ""
#~ "Additional keyword arguments. May contain "
#~ "tensors, although this is not "
#~ "recommended, for the reasons above. The"
#~ " following optional keyword arguments are"
#~ " reserved: - `training`: Boolean scalar "
#~ "tensor of Python boolean indicating   "
#~ "whether the `call` is meant for "
#~ "training or inference. - `mask`: Boolean"
#~ " input mask. If the layer's `call()`"
#~ " method takes a   `mask` argument, "
#~ "its default value will be set to"
#~ " the mask generated   for `inputs` by"
#~ " the previous layer (if `input` did"
#~ " come from a layer   that generated"
#~ " a corresponding mask, i.e. if it "
#~ "came from a Keras   layer with "
#~ "masking support)."
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`~keras.optimizer_v2.learning_rate_schedule.LearningRateSchedule`"
#~ msgstr ""

#~ msgid ""
#~ "Get Pauli string array and weights "
#~ "array for a qubit Hamiltonian as a"
#~ " sum of Pauli strings defined in "
#~ "openfermion ``QubitOperator``."
#~ msgstr ""

#~ msgid "``openfermion.ops.operators.qubit_operator.QubitOperator``"
#~ msgstr ""

#~ msgid "The number of qubits"
#~ msgstr ""

#~ msgid "Pauli String array and weights array"
#~ msgstr ""

#~ msgid "Bases: :py:class:`~keras.src.engine.training.Model`"
#~ msgstr ""

#~ msgid "Optional regularizer function for the output of this layer."
#~ msgstr ""

#~ msgid "Add loss tensor(s), potentially dependent on layer inputs."
#~ msgstr ""

#~ msgid ""
#~ "Some losses (for instance, activity "
#~ "regularization losses) may be dependent "
#~ "on the inputs passed when calling "
#~ "a layer. Hence, when reusing the "
#~ "same layer on different inputs `a` "
#~ "and `b`, some entries in `layer.losses`"
#~ " may be dependent on `a` and "
#~ "some on `b`. This method automatically"
#~ " keeps track of dependencies."
#~ msgstr ""

#~ msgid ""
#~ "This method can be used inside a"
#~ " subclassed layer or model's `call` "
#~ "function, in which case `losses` should"
#~ " be a Tensor or list of "
#~ "Tensors."
#~ msgstr ""

#~ msgid "```python class MyLayer(tf.keras.layers.Layer):"
#~ msgstr ""

#~ msgid "def call(self, inputs):"
#~ msgstr ""

#~ msgid "self.add_loss(tf.abs(tf.reduce_mean(inputs))) return inputs"
#~ msgstr ""

#~ msgid ""
#~ "The same code works in distributed "
#~ "training: the input to `add_loss()` is"
#~ " treated like a regularization loss "
#~ "and averaged across replicas by the "
#~ "training loop (both built-in "
#~ "`Model.fit()` and compliant custom training"
#~ " loops)."
#~ msgstr ""

#~ msgid ""
#~ "The `add_loss` method can also be "
#~ "called directly on a Functional Model"
#~ " during construction. In this case, "
#~ "any loss Tensors passed to this "
#~ "Model must be symbolic and be able"
#~ " to be traced back to the "
#~ "model's `Input`s. These losses become "
#~ "part of the model's topology and "
#~ "are tracked in `get_config`."
#~ msgstr ""

#~ msgid ""
#~ "```python inputs = tf.keras.Input(shape=(10,)) "
#~ "x = tf.keras.layers.Dense(10)(inputs) outputs "
#~ "= tf.keras.layers.Dense(1)(x) model = "
#~ "tf.keras.Model(inputs, outputs) # Activity "
#~ "regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) "
#~ "```"
#~ msgstr ""

#~ msgid ""
#~ "If this is not the case for "
#~ "your loss (if, for example, your "
#~ "loss references a `Variable` of one "
#~ "of the model's layers), you can "
#~ "wrap your loss in a zero-argument"
#~ " lambda. These losses are not tracked"
#~ " as part of the model's topology "
#~ "since they can't be serialized."
#~ msgstr ""

#~ msgid ""
#~ "```python inputs = tf.keras.Input(shape=(10,)) "
#~ "d = tf.keras.layers.Dense(10) x = "
#~ "d(inputs) outputs = tf.keras.layers.Dense(1)(x) "
#~ "model = tf.keras.Model(inputs, outputs) # "
#~ "Weight regularization. model.add_loss(lambda: "
#~ "tf.reduce_mean(d.kernel)) ```"
#~ msgstr ""

#~ msgid ""
#~ "Loss tensor, or list/tuple of tensors."
#~ " Rather than tensors, losses may also"
#~ " be zero-argument callables which "
#~ "create a loss tensor."
#~ msgstr ""

#~ msgid "Used for backwards compatibility only."
#~ msgstr ""

#~ msgid "Adds metric tensor to the layer."
#~ msgstr ""

#~ msgid ""
#~ "This method can be used inside the"
#~ " `call()` method of a subclassed "
#~ "layer or model."
#~ msgstr ""

#~ msgid "```python class MyMetricLayer(tf.keras.layers.Layer):"
#~ msgstr ""

#~ msgid "def __init__(self):"
#~ msgstr ""

#~ msgid ""
#~ "super(MyMetricLayer, self).__init__(name='my_metric_layer') "
#~ "self.mean = tf.keras.metrics.Mean(name='metric_1')"
#~ msgstr ""

#~ msgid ""
#~ "self.add_metric(self.mean(inputs)) "
#~ "self.add_metric(tf.reduce_sum(inputs), name='metric_2') "
#~ "return inputs"
#~ msgstr ""

#~ msgid ""
#~ "This method can also be called "
#~ "directly on a Functional Model during"
#~ " construction. In this case, any "
#~ "tensor passed to this Model must "
#~ "be symbolic and be able to be "
#~ "traced back to the model's `Input`s. "
#~ "These metrics become part of the "
#~ "model's topology and are tracked when"
#~ " you save the model via `save()`."
#~ msgstr ""

#~ msgid ""
#~ "```python inputs = tf.keras.Input(shape=(10,)) "
#~ "x = tf.keras.layers.Dense(10)(inputs) outputs "
#~ "= tf.keras.layers.Dense(1)(x) model = "
#~ "tf.keras.Model(inputs, outputs) "
#~ "model.add_metric(math_ops.reduce_sum(x), name='metric_1') "
#~ "```"
#~ msgstr ""

#~ msgid ""
#~ "Note: Calling `add_metric()` with the "
#~ "result of a metric object on a "
#~ "Functional Model, as shown in the "
#~ "example below, is not supported. This"
#~ " is because we cannot trace the "
#~ "metric result tensor back to the "
#~ "model's inputs."
#~ msgstr ""

#~ msgid ""
#~ "```python inputs = tf.keras.Input(shape=(10,)) "
#~ "x = tf.keras.layers.Dense(10)(inputs) outputs "
#~ "= tf.keras.layers.Dense(1)(x) model = "
#~ "tf.keras.Model(inputs, outputs) "
#~ "model.add_metric(tf.keras.metrics.Mean()(x), name='metric_1')"
#~ " ```"
#~ msgstr ""

#~ msgid "Metric tensor."
#~ msgstr ""

#~ msgid "String metric name."
#~ msgstr ""

#~ msgid ""
#~ "Additional keyword arguments for backward "
#~ "compatibility. Accepted values: `aggregation` "
#~ "- When the `value` tensor provided "
#~ "is not the result of calling a "
#~ "`keras.Metric` instance, it will be "
#~ "aggregated by default using a "
#~ "`keras.Metric.Mean`."
#~ msgstr ""

#~ msgid "Add update op(s), potentially dependent on layer inputs."
#~ msgstr ""

#~ msgid ""
#~ "Weight updates (for instance, the "
#~ "updates of the moving mean and "
#~ "variance in a BatchNormalization layer) "
#~ "may be dependent on the inputs "
#~ "passed when calling a layer. Hence, "
#~ "when reusing the same layer on "
#~ "different inputs `a` and `b`, some "
#~ "entries in `layer.updates` may be "
#~ "dependent on `a` and some on `b`."
#~ " This method automatically keeps track "
#~ "of dependencies."
#~ msgstr ""

#~ msgid ""
#~ "This call is ignored when eager "
#~ "execution is enabled (in that case, "
#~ "variable updates are run on the "
#~ "fly and thus do not need to "
#~ "be tracked for later execution)."
#~ msgstr ""

#~ msgid ""
#~ "Update op, or list/tuple of update "
#~ "ops, or zero-arg callable that "
#~ "returns an update op. A zero-arg"
#~ " callable should be passed in order"
#~ " to disable running the updates by"
#~ " setting `trainable=False` on this Layer,"
#~ " when executing in Eager mode."
#~ msgstr ""

#~ msgid "Deprecated, do NOT use! Alias for `add_weight`."
#~ msgstr ""

#~ msgid "Adds a new variable to the layer."
#~ msgstr ""

#~ msgid "Variable name."
#~ msgstr ""

#~ msgid "Variable shape. Defaults to scalar if unspecified."
#~ msgstr ""

#~ msgid "The type of the variable. Defaults to `self.dtype`."
#~ msgstr ""

#~ msgid "Initializer instance (callable)."
#~ msgstr ""

#~ msgid "Regularizer instance (callable)."
#~ msgstr ""

#~ msgid ""
#~ "Boolean, whether the variable should be"
#~ " part of the layer's "
#~ "\"trainable_variables\" (e.g. variables, biases) "
#~ "or \"non_trainable_variables\" (e.g. BatchNorm "
#~ "mean and variance). Note that "
#~ "`trainable` cannot be `True` if "
#~ "`synchronization` is set to `ON_READ`."
#~ msgstr ""

#~ msgid "Constraint instance (callable)."
#~ msgstr ""

#~ msgid ""
#~ "Whether to use a `ResourceVariable` or"
#~ " not. See [this guide]( "
#~ "https://www.tensorflow.org/guide/migrate/tf1_vs_tf2#resourcevariables_instead_of_referencevariables)"
#~ "  for more information."
#~ msgstr ""

#~ msgid ""
#~ "Whether to use a `ResourceVariable` or"
#~ " not. See [this guide]( "
#~ "https://www.tensorflow.org/guide/migrate/tf1_vs_tf2#resourcevariables_instead_of_referencevariables)"
#~ msgstr ""

#~ msgid "for more information."
#~ msgstr ""

#~ msgid ""
#~ "Indicates when a distributed a variable"
#~ " will be aggregated. Accepted values "
#~ "are constants defined in the class "
#~ "`tf.VariableSynchronization`. By default the "
#~ "synchronization is set to `AUTO` and "
#~ "the current `DistributionStrategy` chooses "
#~ "when to synchronize. If `synchronization` "
#~ "is set to `ON_READ`, `trainable` must"
#~ " not be set to `True`."
#~ msgstr ""

#~ msgid ""
#~ "Indicates how a distributed variable "
#~ "will be aggregated. Accepted values are"
#~ " constants defined in the class "
#~ "`tf.VariableAggregation`."
#~ msgstr ""

#~ msgid ""
#~ "Additional keyword arguments. Accepted values"
#~ " are `getter`, `collections`, "
#~ "`experimental_autocast` and `caching_device`."
#~ msgstr ""

#~ msgid "The variable created."
#~ msgstr ""

#~ msgid ""
#~ "When giving unsupported dtype and no "
#~ "initializer or when     trainable has "
#~ "been set to True with synchronization"
#~ " set as     `ON_READ`."
#~ msgstr ""

#~ msgid "Builds the model based on input shapes received."
#~ msgstr ""

#~ msgid ""
#~ "This is to be used for subclassed"
#~ " models, which do not know at "
#~ "instantiation time what their inputs "
#~ "look like."
#~ msgstr ""

#~ msgid ""
#~ "This method only exists for users "
#~ "who want to call `model.build()` in "
#~ "a standalone way (as a substitute "
#~ "for calling the model on real data"
#~ " to build it). It will never be"
#~ " called by the framework (and thus"
#~ " it will never throw unexpected "
#~ "errors in an unrelated workflow)."
#~ msgstr ""

#~ msgid ""
#~ "Single tuple, `TensorShape` instance, or "
#~ "list/dict of shapes, where shapes are"
#~ " tuples, integers, or `TensorShape` "
#~ "instances."
#~ msgstr ""

#~ msgid ""
#~ "1. In case of invalid user-"
#~ "provided data (not of type tuple,"
#~ "        list, `TensorShape`, or dict).     "
#~ "2. If the model requires call "
#~ "arguments that are agnostic        to "
#~ "the input shapes (positional or keyword"
#~ " arg in call        signature).     3. "
#~ "If not all layers were properly "
#~ "built.     4. If float type inputs "
#~ "are not supported within the layers."
#~ msgstr ""

#~ msgid ""
#~ "In case of invalid user-provided "
#~ "data (not of type tuple,        list,"
#~ " `TensorShape`, or dict).     2. If "
#~ "the model requires call arguments that"
#~ " are agnostic        to the input "
#~ "shapes (positional or keyword arg in "
#~ "call        signature).     3. If not "
#~ "all layers were properly built.     4."
#~ " If float type inputs are not "
#~ "supported within the layers."
#~ msgstr ""

#~ msgid "Input tensor, or dict/list/tuple of input tensors."
#~ msgstr ""

#~ msgid ""
#~ "Boolean or boolean scalar tensor, "
#~ "indicating whether to run the `Network`"
#~ " in training mode or inference mode."
#~ msgstr ""

#~ msgid ""
#~ "A mask or list of masks. A "
#~ "mask can be either a boolean "
#~ "tensor or None (no mask). For more"
#~ " details, check the guide "
#~ "[here](https://www.tensorflow.org/guide/keras/masking_and_padding)."
#~ msgstr ""

#~ msgid ""
#~ "```python "
#~ "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),"
#~ msgstr ""

#~ msgid ""
#~ "loss=tf.keras.losses.BinaryCrossentropy(), "
#~ "metrics=[tf.keras.metrics.BinaryAccuracy(),"
#~ msgstr ""

#~ msgid "tf.keras.metrics.FalseNegatives()])"
#~ msgstr ""

#~ msgid ""
#~ "String (name of optimizer) or optimizer"
#~ " instance. See `tf.keras.optimizers`."
#~ msgstr ""

#~ msgid ""
#~ "Loss function. May be a string "
#~ "(name of loss function), or a "
#~ "`tf.keras.losses.Loss` instance. See "
#~ "`tf.keras.losses`. A loss function is "
#~ "any callable with the signature `loss"
#~ " = fn(y_true, y_pred)`, where `y_true` "
#~ "are the ground truth values, and "
#~ "`y_pred` are the model's predictions. "
#~ "`y_true` should have shape `(batch_size, "
#~ "d0, .. dN)` (except in the case"
#~ " of sparse loss functions such as "
#~ "sparse categorical crossentropy which expects"
#~ " integer arrays of shape `(batch_size, "
#~ "d0, .. dN-1)`). `y_pred` should have "
#~ "shape `(batch_size, d0, .. dN)`. The "
#~ "loss function should return a float "
#~ "tensor. If a custom `Loss` instance "
#~ "is used and reduction is set to"
#~ " `None`, return value has shape "
#~ "`(batch_size, d0, .. dN-1)` i.e. per-"
#~ "sample or per-timestep loss values; "
#~ "otherwise, it is a scalar. If the"
#~ " model has multiple outputs, you can"
#~ " use a different loss on each "
#~ "output by passing a dictionary or "
#~ "a list of losses. The loss value"
#~ " that will be minimized by the "
#~ "model will then be the sum of "
#~ "all individual losses, unless `loss_weights`"
#~ " is specified."
#~ msgstr ""

#~ msgid ""
#~ "List of metrics to be evaluated by"
#~ " the model during training and "
#~ "testing. Each of this can be a "
#~ "string (name of a built-in "
#~ "function), function or a "
#~ "`tf.keras.metrics.Metric` instance. See "
#~ "`tf.keras.metrics`. Typically you will use "
#~ "`metrics=['accuracy']`. A function is any "
#~ "callable with the signature `result ="
#~ " fn(y_true, y_pred)`. To specify different"
#~ " metrics for different outputs of a"
#~ " multi-output model, you could also"
#~ " pass a dictionary, such as "
#~ "`metrics={'output_a':'accuracy', 'output_b':['accuracy', "
#~ "'mse']}`. You can also pass a list"
#~ " to specify a metric or a list"
#~ " of metrics for each output, such "
#~ "as `metrics=[['accuracy'], ['accuracy', 'mse']]` "
#~ "or `metrics=['accuracy', ['accuracy', 'mse']]`. "
#~ "When you pass the strings 'accuracy' "
#~ "or 'acc', we convert this to one"
#~ " of `tf.keras.metrics.BinaryAccuracy`, "
#~ "`tf.keras.metrics.CategoricalAccuracy`, "
#~ "`tf.keras.metrics.SparseCategoricalAccuracy` based on "
#~ "the shapes of the targets and of"
#~ " the model output. We do a "
#~ "similar conversion for the strings "
#~ "'crossentropy' and 'ce' as well. The "
#~ "metrics passed here are evaluated "
#~ "without sample weighting; if you would"
#~ " like sample weighting to apply, you"
#~ " can specify your metrics via the "
#~ "`weighted_metrics` argument instead."
#~ msgstr ""

#~ msgid ""
#~ "Bool. If `True`, this `Model`'s logic"
#~ " will not be wrapped in a "
#~ "`tf.function`. Recommended to leave this "
#~ "as `None` unless your `Model` cannot "
#~ "be run inside a `tf.function`. "
#~ "`run_eagerly=True` is not supported when "
#~ "using `tf.distribute.experimental.ParameterServerStrategy`. "
#~ "Defaults to  `False`."
#~ msgstr ""

#~ msgid ""
#~ "Bool. If `True`, this `Model`'s logic"
#~ " will not be wrapped in a "
#~ "`tf.function`. Recommended to leave this "
#~ "as `None` unless your `Model` cannot "
#~ "be run inside a `tf.function`. "
#~ "`run_eagerly=True` is not supported when "
#~ "using `tf.distribute.experimental.ParameterServerStrategy`. "
#~ "Defaults to"
#~ msgstr ""

#~ msgid "`False`."
#~ msgstr ""

#~ msgid ""
#~ "Int. The number of batches to run"
#~ " during each `tf.function` call. Running"
#~ " multiple batches inside a single "
#~ "`tf.function` call can greatly improve "
#~ "performance on TPUs or small models "
#~ "with a large Python overhead. At "
#~ "most, one full epoch will be run"
#~ " each execution. If a number larger"
#~ " than the size of the epoch is"
#~ " passed, the execution will be "
#~ "truncated to the size of the "
#~ "epoch. Note that if `steps_per_execution` "
#~ "is set to `N`, `Callback.on_batch_begin` "
#~ "and `Callback.on_batch_end` methods will only"
#~ " be called every `N` batches (i.e."
#~ " before/after each `tf.function` execution). "
#~ "Defaults to `1`."
#~ msgstr ""

#~ msgid ""
#~ "If `True`, compile the model training"
#~ " step with XLA. "
#~ "[XLA](https://www.tensorflow.org/xla) is an "
#~ "optimizing compiler for machine learning. "
#~ "`jit_compile` is not enabled for by "
#~ "default. Note that `jit_compile=True` may "
#~ "not necessarily work for all models. "
#~ "For more information on supported "
#~ "operations please refer to the [XLA "
#~ "documentation](https://www.tensorflow.org/xla). Also refer"
#~ " to [known XLA "
#~ "issues](https://www.tensorflow.org/xla/known_issues) for "
#~ "more details."
#~ msgstr ""

#~ msgid ""
#~ "Integer or 'auto'. Used for "
#~ "`tf.distribute.ParameterServerStrategy` training only. "
#~ "This arg sets the number of shards"
#~ " to split the dataset into, to "
#~ "enable an exact visitation guarantee for"
#~ " evaluation, meaning the model will "
#~ "be applied to each dataset element "
#~ "exactly once, even if workers fail. "
#~ "The dataset must be sharded to "
#~ "ensure separate workers do not process"
#~ " the same data. The number of "
#~ "shards should be at least the "
#~ "number of workers for good performance."
#~ " A value of 'auto' turns on "
#~ "exact evaluation and uses a heuristic"
#~ " for the number of shards based "
#~ "on the number of workers. 0, "
#~ "meaning no visitation guarantee is "
#~ "provided. NOTE: Custom implementations of "
#~ "`Model.test_step` will be ignored when "
#~ "doing exact evaluation. Defaults to `0`."
#~ msgstr ""

#~ msgid "Arguments supported for backwards compatibility only."
#~ msgstr ""

#~ msgid "The dtype of the layer's computations."
#~ msgstr ""

#~ msgid ""
#~ "This is equivalent to "
#~ "`Layer.dtype_policy.compute_dtype`. Unless mixed "
#~ "precision is used, this is the "
#~ "same as `Layer.dtype`, the dtype of "
#~ "the weights."
#~ msgstr ""

#~ msgid ""
#~ "Layers automatically cast their inputs "
#~ "to the compute dtype, which causes "
#~ "computations and the output to be "
#~ "in the compute dtype as well. This"
#~ " is done by the base Layer "
#~ "class in `Layer.__call__`, so you do "
#~ "not have to insert these casts if"
#~ " implementing your own layer."
#~ msgstr ""

#~ msgid ""
#~ "Layers often perform certain internal "
#~ "computations in higher precision when "
#~ "`compute_dtype` is float16 or bfloat16 "
#~ "for numeric stability. The output will"
#~ " still typically be float16 or "
#~ "bfloat16 in such cases."
#~ msgstr ""

#~ msgid "The layer's compute dtype."
#~ msgstr ""

#~ msgid "Example: ```python class MyModel(tf.keras.Model):"
#~ msgstr ""

#~ msgid ""
#~ "super(MyModel, self).__init__(*args, **kwargs) "
#~ "self.loss_tracker = tf.keras.metrics.Mean(name='loss')"
#~ msgstr ""

#~ msgid "def compute_loss(self, x, y, y_pred, sample_weight):"
#~ msgstr ""

#~ msgid ""
#~ "loss = tf.reduce_mean(tf.math.squared_difference(y_pred,"
#~ " y)) loss += tf.add_n(self.losses) "
#~ "self.loss_tracker.update_state(loss) return loss"
#~ msgstr ""

#~ msgid "self.loss_tracker.reset_states()"
#~ msgstr ""

#~ msgid ""
#~ "tensors = tf.random.uniform((10, 10)), "
#~ "tf.random.uniform((10,)) dataset = "
#~ "tf.data.Dataset.from_tensor_slices(tensors).repeat().batch(1)"
#~ msgstr ""

#~ msgid ""
#~ "inputs = tf.keras.layers.Input(shape=(10,), "
#~ "name='my_input') outputs = "
#~ "tf.keras.layers.Dense(10)(inputs) model = "
#~ "MyModel(inputs, outputs) "
#~ "model.add_loss(tf.reduce_sum(outputs))"
#~ msgstr ""

#~ msgid ""
#~ "optimizer = tf.keras.optimizers.SGD() "
#~ "model.compile(optimizer, loss='mse', "
#~ "steps_per_execution=10) model.fit(dataset, epochs=2, "
#~ "steps_per_epoch=10) print('My custom loss: ',"
#~ " model.loss_tracker.result().numpy()) ```"
#~ msgstr ""

#~ msgid ""
#~ "The total loss as a `tf.Tensor`, "
#~ "or `None` if no loss results "
#~ "(which is the case when called by"
#~ " `Model.test_step`)."
#~ msgstr ""

#~ msgid "Computes an output mask tensor."
#~ msgstr ""

#~ msgid "Tensor or list of tensors."
#~ msgstr ""

#~ msgid ""
#~ "None or a tensor (or list of "
#~ "tensors,     one per output tensor of"
#~ " the layer)."
#~ msgstr ""

#~ msgid "None or a tensor (or list of tensors,"
#~ msgstr ""

#~ msgid "one per output tensor of the layer)."
#~ msgstr ""

#~ msgid ""
#~ "Subclasses can optionally override this "
#~ "method to provide custom metric updating"
#~ " and collection logic."
#~ msgstr ""

#~ msgid "Example: ```python class MyModel(tf.keras.Sequential):"
#~ msgstr ""

#~ msgid ""
#~ "# This super call updates "
#~ "`self.compiled_metrics` and returns # results"
#~ " for all metrics listed in "
#~ "`self.metrics`. metric_results = super(MyModel, "
#~ "self).compute_metrics("
#~ msgstr ""

#~ msgid ""
#~ "# Note that `self.custom_metric` is not"
#~ " listed in `self.metrics`. "
#~ "self.custom_metric.update_state(x, y, y_pred, "
#~ "sample_weight) metric_results['custom_metric_name'] = "
#~ "self.custom_metric.result() return metric_results"
#~ msgstr ""

#~ msgid "Predictions returned by the model (output of `model.call(x)`)"
#~ msgstr ""

#~ msgid ""
#~ "A `dict` containing values that will "
#~ "be passed to "
#~ "`tf.keras.callbacks.CallbackList.on_train_batch_end()`. "
#~ "Typically, the values of the metrics "
#~ "listed in `self.metrics` are returned. "
#~ "Example: `{'loss': 0.2, 'accuracy': 0.7}`."
#~ msgstr ""

#~ msgid "Computes the output shape of the layer."
#~ msgstr ""

#~ msgid ""
#~ "This method will cause the layer's "
#~ "state to be built, if that has "
#~ "not happened before. This requires that"
#~ " the layer will later be used "
#~ "with inputs that match the input "
#~ "shape provided here."
#~ msgstr ""

#~ msgid ""
#~ "Shape tuple (tuple of integers) or "
#~ "`tf.TensorShape`, or structure of shape "
#~ "tuples / `tf.TensorShape` instances (one "
#~ "per output tensor of the layer). "
#~ "Shape tuples can include None for "
#~ "free dimensions, instead of an integer."
#~ msgstr ""

#~ msgid "A `tf.TensorShape` instance or structure of `tf.TensorShape` instances."
#~ msgstr ""

#~ msgid "Compute the output tensor signature of the layer based on the inputs."
#~ msgstr ""

#~ msgid ""
#~ "Unlike a TensorShape object, a "
#~ "TensorSpec object contains both shape "
#~ "and dtype information for a tensor. "
#~ "This method allows layers to provide "
#~ "output dtype information if it is "
#~ "different from the input dtype. For "
#~ "any layer that doesn't implement this"
#~ " function, the framework will fall "
#~ "back to use `compute_output_shape`, and "
#~ "will assume that the output dtype "
#~ "matches the input dtype."
#~ msgstr ""

#~ msgid ""
#~ "Single TensorSpec or nested structure of"
#~ " TensorSpec objects, describing a candidate"
#~ " input for the layer."
#~ msgstr ""

#~ msgid ""
#~ "Single TensorSpec or nested structure of"
#~ " TensorSpec objects,   describing how the"
#~ " layer would transform the provided "
#~ "input."
#~ msgstr ""

#~ msgid "Single TensorSpec or nested structure of TensorSpec objects,"
#~ msgstr ""

#~ msgid "describing how the layer would transform the provided input."
#~ msgstr ""

#~ msgid "If input_signature contains a non-TensorSpec object."
#~ msgstr ""

#~ msgid ""
#~ "if the layer isn't yet built     "
#~ "(in which case its weights aren't "
#~ "yet defined)."
#~ msgstr ""

#~ msgid "The method employed to reduce per-replica values during training."
#~ msgstr ""

#~ msgid ""
#~ "Unless specified, the value \"auto\" "
#~ "will be assumed, indicating that the "
#~ "reduction strategy should be chosen "
#~ "based on the current running "
#~ "environment. See `reduce_per_replica` function "
#~ "for more details."
#~ msgstr ""

#~ msgid "The `tf.distribute.Strategy` this model was created under."
#~ msgstr ""

#~ msgid "The dtype of the layer weights."
#~ msgstr ""

#~ msgid ""
#~ "This is equivalent to "
#~ "`Layer.dtype_policy.variable_dtype`. Unless mixed "
#~ "precision is used, this is the "
#~ "same as `Layer.compute_dtype`, the dtype "
#~ "of the layer's computations."
#~ msgstr ""

#~ msgid "The dtype policy associated with this layer."
#~ msgstr ""

#~ msgid "This is an instance of a `tf.keras.mixed_precision.Policy`."
#~ msgstr ""

#~ msgid "Whether the layer is dynamic (eager-only); set in the constructor."
#~ msgstr ""

#~ msgid ""
#~ "Input data. It could be: - A "
#~ "Numpy array (or array-like), or a"
#~ " list of arrays   (in case the "
#~ "model has multiple inputs). - A "
#~ "TensorFlow tensor, or a list of "
#~ "tensors   (in case the model has "
#~ "multiple inputs). - A dict mapping "
#~ "input names to the corresponding "
#~ "array/tensors,   if the model has named"
#~ " inputs. - A `tf.data` dataset. "
#~ "Should return a tuple   of either "
#~ "`(inputs, targets)` or   `(inputs, targets,"
#~ " sample_weights)`. - A generator or "
#~ "`keras.utils.Sequence` returning `(inputs,   "
#~ "targets)` or `(inputs, targets, "
#~ "sample_weights)`. A more detailed description"
#~ " of unpacking behavior for iterator "
#~ "types (Dataset, generator, Sequence) is "
#~ "given in the `Unpacking behavior for "
#~ "iterator-like inputs` section of "
#~ "`Model.fit`."
#~ msgstr ""

#~ msgid ""
#~ "Input data. It could be: - A "
#~ "Numpy array (or array-like), or a"
#~ " list of arrays"
#~ msgstr ""

#~ msgid "(in case the model has multiple inputs)."
#~ msgstr ""

#~ msgid ""
#~ "A TensorFlow tensor, or a list of"
#~ " tensors (in case the model has "
#~ "multiple inputs)."
#~ msgstr ""

#~ msgid ""
#~ "A dict mapping input names to the"
#~ " corresponding array/tensors, if the model"
#~ " has named inputs."
#~ msgstr ""

#~ msgid ""
#~ "A `tf.data` dataset. Should return a "
#~ "tuple of either `(inputs, targets)` or"
#~ " `(inputs, targets, sample_weights)`."
#~ msgstr ""

#~ msgid ""
#~ "A generator or `keras.utils.Sequence` "
#~ "returning `(inputs, targets)` or `(inputs, "
#~ "targets, sample_weights)`."
#~ msgstr ""

#~ msgid ""
#~ "A more detailed description of unpacking"
#~ " behavior for iterator types (Dataset, "
#~ "generator, Sequence) is given in the "
#~ "`Unpacking behavior for iterator-like "
#~ "inputs` section of `Model.fit`."
#~ msgstr ""

#~ msgid ""
#~ "Target data. Like the input data "
#~ "`x`, it could be either Numpy "
#~ "array(s) or TensorFlow tensor(s). It "
#~ "should be consistent with `x` (you "
#~ "cannot have Numpy inputs and tensor "
#~ "targets, or inversely). If `x` is "
#~ "a dataset, generator or `keras.utils.Sequence`"
#~ " instance, `y` should not be "
#~ "specified (since targets will be "
#~ "obtained from the iterator/dataset)."
#~ msgstr ""

#~ msgid ""
#~ "Integer or `None`. Number of samples "
#~ "per batch of computation. If "
#~ "unspecified, `batch_size` will default to "
#~ "32. Do not specify the `batch_size` "
#~ "if your data is in the form "
#~ "of a dataset, generators, or "
#~ "`keras.utils.Sequence` instances (since they "
#~ "generate batches)."
#~ msgstr ""

#~ msgid ""
#~ "`\"auto\"`, 0, 1, or 2. Verbosity "
#~ "mode. 0 = silent, 1 = progress "
#~ "bar, 2 = single line. `\"auto\"` "
#~ "becomes 1 for most cases, and to"
#~ " 2 when used with "
#~ "`ParameterServerStrategy`. Note that the "
#~ "progress bar is not particularly useful"
#~ " when logged to a file, so "
#~ "`verbose=2` is recommended when not "
#~ "running interactively (e.g. in a "
#~ "production environment). Defaults to 'auto'."
#~ msgstr ""

#~ msgid ""
#~ "Optional Numpy array of weights for "
#~ "the test samples, used for weighting "
#~ "the loss function. You can either "
#~ "pass a flat (1D) Numpy array with"
#~ " the same length as the input "
#~ "samples   (1:1 mapping between weights "
#~ "and samples), or in the case of"
#~ "     temporal data, you can pass a"
#~ " 2D array with shape `(samples,     "
#~ "sequence_length)`, to apply a different "
#~ "weight to every     timestep of every"
#~ " sample. This argument is not "
#~ "supported when     `x` is a dataset, "
#~ "instead pass sample weights as the "
#~ "third     element of `x`."
#~ msgstr ""

#~ msgid ""
#~ "Optional Numpy array of weights for "
#~ "the test samples, used for weighting "
#~ "the loss function. You can either "
#~ "pass a flat (1D) Numpy array with"
#~ " the same length as the input "
#~ "samples"
#~ msgstr ""

#~ msgid "(1:1 mapping between weights and samples), or in the case of"
#~ msgstr ""

#~ msgid ""
#~ "temporal data, you can pass a 2D"
#~ " array with shape `(samples, "
#~ "sequence_length)`, to apply a different "
#~ "weight to every timestep of every "
#~ "sample. This argument is not supported"
#~ " when `x` is a dataset, instead "
#~ "pass sample weights as the third "
#~ "element of `x`."
#~ msgstr ""

#~ msgid ""
#~ "Integer or `None`. Total number of "
#~ "steps (batches of samples) before "
#~ "declaring the evaluation round finished. "
#~ "Ignored with the default value of "
#~ "`None`. If x is a `tf.data` "
#~ "dataset and `steps` is None, 'evaluate'"
#~ " will run until the dataset is "
#~ "exhausted. This argument is not "
#~ "supported with array inputs."
#~ msgstr ""

#~ msgid ""
#~ "List of `keras.callbacks.Callback` instances. "
#~ "List of callbacks to apply during "
#~ "evaluation. See "
#~ "[callbacks](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks)."
#~ msgstr ""

#~ msgid ""
#~ "Integer. Used for generator or "
#~ "`keras.utils.Sequence` input only. Maximum "
#~ "size for the generator queue. If "
#~ "unspecified, `max_queue_size` will default to"
#~ " 10."
#~ msgstr ""

#~ msgid ""
#~ "Integer. Used for generator or "
#~ "`keras.utils.Sequence` input only. Maximum "
#~ "number of processes to spin up "
#~ "when using process-based threading. If"
#~ " unspecified, `workers` will default to "
#~ "1."
#~ msgstr ""

#~ msgid ""
#~ "Boolean. Used for generator or "
#~ "`keras.utils.Sequence` input only. If `True`,"
#~ " use process-based threading. If "
#~ "unspecified, `use_multiprocessing` will default "
#~ "to `False`. Note that because this "
#~ "implementation relies on multiprocessing, you"
#~ " should not pass non-picklable "
#~ "arguments to the generator as they "
#~ "can't be passed easily to children "
#~ "processes."
#~ msgstr ""

#~ msgid "Unused at this time."
#~ msgstr ""

#~ msgid ""
#~ "See the discussion of `Unpacking "
#~ "behavior for iterator-like inputs` for"
#~ " `Model.fit`."
#~ msgstr ""

#~ msgid ""
#~ "Scalar test loss (if the model has"
#~ " a single output and no metrics) "
#~ "or list of scalars (if the model"
#~ " has multiple outputs and/or metrics). "
#~ "The attribute `model.metrics_names` will give"
#~ " you the display labels for the "
#~ "scalar outputs."
#~ msgstr ""

#~ msgid "If `model.evaluate` is wrapped in a `tf.function`."
#~ msgstr ""

#~ msgid "Evaluates the model on a data generator."
#~ msgstr ""

#~ msgid "DEPRECATED:"
#~ msgstr ""

#~ msgid ""
#~ "`Model.evaluate` now supports generators, so"
#~ " there is no longer any need to"
#~ " use this endpoint."
#~ msgstr ""

#~ msgid "Create a SavedModel artifact for inference (e.g. via TF-Serving)."
#~ msgstr ""

#~ msgid ""
#~ "This method lets you export a "
#~ "model to a lightweight SavedModel "
#~ "artifact that contains the model's "
#~ "forward pass only (its `call()` method)"
#~ " and can be served via e.g. "
#~ "TF-Serving. The forward pass is "
#~ "registered under the name `serve()` (see"
#~ " example below)."
#~ msgstr ""

#~ msgid ""
#~ "The original code of the model "
#~ "(including any custom layers you may "
#~ "have used) is *no longer* necessary "
#~ "to reload the artifact -- it is"
#~ " entirely standalone."
#~ msgstr ""

#~ msgid "`str` or `pathlib.Path` object. Path where to save the artifact."
#~ msgstr ""

#~ msgid "```python # Create the artifact model.export(\"path/to/location\")"
#~ msgstr ""

#~ msgid ""
#~ "# Later, in a different process /"
#~ " environment... reloaded_artifact = "
#~ "tf.saved_model.load(\"path/to/location\") predictions ="
#~ " reloaded_artifact.serve(input_data) ```"
#~ msgstr ""

#~ msgid ""
#~ "If you would like to customize "
#~ "your serving endpoints, you can use "
#~ "the lower-level `keras.export.ExportArchive` "
#~ "class. The `export()` method relies on"
#~ " `ExportArchive` internally."
#~ msgstr ""

#~ msgid "Finalizes the layers state after updating layer weights."
#~ msgstr ""

#~ msgid ""
#~ "This function can be subclassed in "
#~ "a layer and will be called after"
#~ " updating a layer weights. It can "
#~ "be overridden to finalize any additional"
#~ " layer state after a weight update."
#~ msgstr ""

#~ msgid ""
#~ "This function will be called after "
#~ "weights of a layer have been "
#~ "restored from a loaded model."
#~ msgstr ""

#~ msgid ""
#~ "Input data. It could be: - A "
#~ "Numpy array (or array-like), or a"
#~ " list of arrays   (in case the "
#~ "model has multiple inputs). - A "
#~ "TensorFlow tensor, or a list of "
#~ "tensors   (in case the model has "
#~ "multiple inputs). - A dict mapping "
#~ "input names to the corresponding "
#~ "array/tensors,   if the model has named"
#~ " inputs. - A `tf.data` dataset. "
#~ "Should return a tuple   of either "
#~ "`(inputs, targets)` or   `(inputs, targets,"
#~ " sample_weights)`. - A generator or "
#~ "`keras.utils.Sequence` returning `(inputs,   "
#~ "targets)` or `(inputs, targets, "
#~ "sample_weights)`. - A "
#~ "`tf.keras.utils.experimental.DatasetCreator`, which wraps"
#~ " a   callable that takes a single "
#~ "argument of type   `tf.distribute.InputContext`, "
#~ "and returns a `tf.data.Dataset`.   "
#~ "`DatasetCreator` should be used when "
#~ "users prefer to specify the   per-"
#~ "replica batching and sharding logic for"
#~ " the `Dataset`.   See "
#~ "`tf.keras.utils.experimental.DatasetCreator` doc for "
#~ "more   information. A more detailed "
#~ "description of unpacking behavior for "
#~ "iterator types (Dataset, generator, Sequence)"
#~ " is given below. If these include "
#~ "`sample_weights` as a third component, "
#~ "note that sample weighting applies to"
#~ " the `weighted_metrics` argument but not"
#~ " the `metrics` argument in `compile()`. "
#~ "If using "
#~ "`tf.distribute.experimental.ParameterServerStrategy`, only "
#~ "`DatasetCreator` type is supported for "
#~ "`x`."
#~ msgstr ""

#~ msgid ""
#~ "A `tf.keras.utils.experimental.DatasetCreator`, which "
#~ "wraps a callable that takes a "
#~ "single argument of type "
#~ "`tf.distribute.InputContext`, and returns a "
#~ "`tf.data.Dataset`. `DatasetCreator` should be "
#~ "used when users prefer to specify "
#~ "the per-replica batching and sharding"
#~ " logic for the `Dataset`. See "
#~ "`tf.keras.utils.experimental.DatasetCreator` doc for "
#~ "more information."
#~ msgstr ""

#~ msgid ""
#~ "A more detailed description of unpacking"
#~ " behavior for iterator types (Dataset, "
#~ "generator, Sequence) is given below. If"
#~ " these include `sample_weights` as a "
#~ "third component, note that sample "
#~ "weighting applies to the `weighted_metrics`"
#~ " argument but not the `metrics` "
#~ "argument in `compile()`. If using "
#~ "`tf.distribute.experimental.ParameterServerStrategy`, only "
#~ "`DatasetCreator` type is supported for "
#~ "`x`."
#~ msgstr ""

#~ msgid ""
#~ "Target data. Like the input data "
#~ "`x`, it could be either Numpy "
#~ "array(s) or TensorFlow tensor(s). It "
#~ "should be consistent with `x` (you "
#~ "cannot have Numpy inputs and tensor "
#~ "targets, or inversely). If `x` is "
#~ "a dataset, generator, or "
#~ "`keras.utils.Sequence` instance, `y` should "
#~ "not be specified (since targets will "
#~ "be obtained from `x`)."
#~ msgstr ""

#~ msgid ""
#~ "Integer or `None`. Number of samples "
#~ "per gradient update. If unspecified, "
#~ "`batch_size` will default to 32. Do "
#~ "not specify the `batch_size` if your "
#~ "data is in the form of datasets,"
#~ " generators, or `keras.utils.Sequence` instances"
#~ " (since they generate batches)."
#~ msgstr ""

#~ msgid ""
#~ "'auto', 0, 1, or 2. Verbosity "
#~ "mode. 0 = silent, 1 = progress "
#~ "bar, 2 = one line per epoch. "
#~ "'auto' becomes 1 for most cases, "
#~ "but 2 when used with "
#~ "`ParameterServerStrategy`. Note that the "
#~ "progress bar is not particularly useful"
#~ " when logged to a file, so "
#~ "verbose=2 is recommended when not "
#~ "running interactively (eg, in a "
#~ "production environment). Defaults to 'auto'."
#~ msgstr ""

#~ msgid ""
#~ "List of `keras.callbacks.Callback` instances. "
#~ "List of callbacks to apply during "
#~ "training. See `tf.keras.callbacks`. Note "
#~ "`tf.keras.callbacks.ProgbarLogger` and "
#~ "`tf.keras.callbacks.History` callbacks are created"
#~ " automatically and need not be passed"
#~ " into `model.fit`. `tf.keras.callbacks.ProgbarLogger`"
#~ " is created or not based on "
#~ "`verbose` argument to `model.fit`. Callbacks"
#~ " with batch-level calls are currently"
#~ " unsupported with "
#~ "`tf.distribute.experimental.ParameterServerStrategy`, and "
#~ "users are advised to implement epoch-"
#~ "level calls instead with an appropriate"
#~ " `steps_per_epoch` value."
#~ msgstr ""

#~ msgid ""
#~ "Float between 0 and 1. Fraction of"
#~ " the training data to be used "
#~ "as validation data. The model will "
#~ "set apart this fraction of the "
#~ "training data, will not train on "
#~ "it, and will evaluate the loss and"
#~ " any model metrics on this data "
#~ "at the end of each epoch. The "
#~ "validation data is selected from the "
#~ "last samples in the `x` and `y`"
#~ " data provided, before shuffling. This "
#~ "argument is not supported when `x` "
#~ "is a dataset, generator or "
#~ "`keras.utils.Sequence` instance. If both "
#~ "`validation_data` and `validation_split` are "
#~ "provided, `validation_data` will override "
#~ "`validation_split`. `validation_split` is not "
#~ "yet supported with "
#~ "`tf.distribute.experimental.ParameterServerStrategy`."
#~ msgstr ""

#~ msgid ""
#~ "Data on which to evaluate the loss"
#~ " and any model metrics at the "
#~ "end of each epoch. The model will"
#~ " not be trained on this data. "
#~ "Thus, note the fact that the "
#~ "validation loss of data provided using"
#~ " `validation_split` or `validation_data` is "
#~ "not affected by regularization layers "
#~ "like noise and dropout. `validation_data` "
#~ "will override `validation_split`. `validation_data`"
#~ " could be:   - A tuple `(x_val, "
#~ "y_val)` of Numpy arrays or tensors."
#~ "   - A tuple `(x_val, y_val, "
#~ "val_sample_weights)` of NumPy     arrays.   -"
#~ " A `tf.data.Dataset`.   - A Python "
#~ "generator or `keras.utils.Sequence` returning   "
#~ "`(inputs, targets)` or `(inputs, targets, "
#~ "sample_weights)`. `validation_data` is not yet"
#~ " supported with "
#~ "`tf.distribute.experimental.ParameterServerStrategy`."
#~ msgstr ""

#~ msgid ""
#~ "Data on which to evaluate the loss"
#~ " and any model metrics at the "
#~ "end of each epoch. The model will"
#~ " not be trained on this data. "
#~ "Thus, note the fact that the "
#~ "validation loss of data provided using"
#~ " `validation_split` or `validation_data` is "
#~ "not affected by regularization layers "
#~ "like noise and dropout. `validation_data` "
#~ "will override `validation_split`. `validation_data`"
#~ " could be:"
#~ msgstr ""

#~ msgid "A tuple `(x_val, y_val)` of Numpy arrays or tensors."
#~ msgstr ""

#~ msgid "A tuple `(x_val, y_val, val_sample_weights)` of NumPy arrays."
#~ msgstr ""

#~ msgid "A `tf.data.Dataset`."
#~ msgstr ""

#~ msgid "A Python generator or `keras.utils.Sequence` returning"
#~ msgstr ""

#~ msgid "`(inputs, targets)` or `(inputs, targets, sample_weights)`."
#~ msgstr ""

#~ msgid ""
#~ "`validation_data` is not yet supported "
#~ "with `tf.distribute.experimental.ParameterServerStrategy`."
#~ msgstr ""

#~ msgid ""
#~ "Boolean (whether to shuffle the training"
#~ " data before each epoch) or str "
#~ "(for 'batch'). This argument is ignored"
#~ " when `x` is a generator or an"
#~ " object of tf.data.Dataset. 'batch' is "
#~ "a special option for dealing with "
#~ "the limitations of HDF5 data; it "
#~ "shuffles in batch-sized chunks. Has "
#~ "no effect when `steps_per_epoch` is not"
#~ " `None`."
#~ msgstr ""

#~ msgid ""
#~ "Optional Numpy array of weights for "
#~ "the training samples, used for weighting"
#~ " the loss function (during training "
#~ "only). You can either pass a flat"
#~ " (1D) Numpy array with the same "
#~ "length as the input samples (1:1 "
#~ "mapping between weights and samples), or"
#~ " in the case of temporal data, "
#~ "you can pass a 2D array with "
#~ "shape `(samples, sequence_length)`, to apply"
#~ " a different weight to every timestep"
#~ " of every sample. This argument is"
#~ " not supported when `x` is a "
#~ "dataset, generator, or `keras.utils.Sequence` "
#~ "instance, instead provide the sample_weights"
#~ " as the third element of `x`. "
#~ "Note that sample weighting does not "
#~ "apply to metrics specified via the "
#~ "`metrics` argument in `compile()`. To "
#~ "apply sample weighting to your metrics,"
#~ " you can specify them via the "
#~ "`weighted_metrics` in `compile()` instead."
#~ msgstr ""

#~ msgid ""
#~ "Integer or `None`. Total number of "
#~ "steps (batches of samples) before "
#~ "declaring one epoch finished and "
#~ "starting the next epoch. When training"
#~ " with input tensors such as "
#~ "TensorFlow data tensors, the default "
#~ "`None` is equal to the number of"
#~ " samples in your dataset divided by"
#~ " the batch size, or 1 if that"
#~ " cannot be determined. If x is "
#~ "a `tf.data` dataset, and 'steps_per_epoch' "
#~ "is None, the epoch will run until"
#~ " the input dataset is exhausted.  "
#~ "When passing an infinitely repeating "
#~ "dataset, you must specify the "
#~ "`steps_per_epoch` argument. If `steps_per_epoch=-1`"
#~ " the training will run indefinitely "
#~ "with an infinitely repeating dataset.  "
#~ "This argument is not supported with "
#~ "array inputs. When using "
#~ "`tf.distribute.experimental.ParameterServerStrategy`:   * "
#~ "`steps_per_epoch=None` is not supported."
#~ msgstr ""

#~ msgid ""
#~ "Integer or `None`. Total number of "
#~ "steps (batches of samples) before "
#~ "declaring one epoch finished and "
#~ "starting the next epoch. When training"
#~ " with input tensors such as "
#~ "TensorFlow data tensors, the default "
#~ "`None` is equal to the number of"
#~ " samples in your dataset divided by"
#~ " the batch size, or 1 if that"
#~ " cannot be determined. If x is "
#~ "a `tf.data` dataset, and 'steps_per_epoch' "
#~ "is None, the epoch will run until"
#~ " the input dataset is exhausted.  "
#~ "When passing an infinitely repeating "
#~ "dataset, you must specify the "
#~ "`steps_per_epoch` argument. If `steps_per_epoch=-1`"
#~ " the training will run indefinitely "
#~ "with an infinitely repeating dataset.  "
#~ "This argument is not supported with "
#~ "array inputs. When using "
#~ "`tf.distribute.experimental.ParameterServerStrategy`:"
#~ msgstr ""

#~ msgid "`steps_per_epoch=None` is not supported."
#~ msgstr ""

#~ msgid ""
#~ "Only relevant if `validation_data` is "
#~ "provided and is a `tf.data` dataset. "
#~ "Total number of steps (batches of "
#~ "samples) to draw before stopping when"
#~ " performing validation at the end of"
#~ " every epoch. If 'validation_steps' is "
#~ "None, validation will run until the "
#~ "`validation_data` dataset is exhausted. In "
#~ "the case of an infinitely repeated "
#~ "dataset, it will run into an "
#~ "infinite loop. If 'validation_steps' is "
#~ "specified and only part of the "
#~ "dataset will be consumed, the evaluation"
#~ " will start from the beginning of "
#~ "the dataset at each epoch. This "
#~ "ensures that the same validation samples"
#~ " are used every time."
#~ msgstr ""

#~ msgid ""
#~ "Integer or `None`. Number of samples "
#~ "per validation batch. If unspecified, "
#~ "will default to `batch_size`. Do not "
#~ "specify the `validation_batch_size` if your"
#~ " data is in the form of "
#~ "datasets, generators, or `keras.utils.Sequence` "
#~ "instances (since they generate batches)."
#~ msgstr ""

#~ msgid ""
#~ "Only relevant if validation data is "
#~ "provided. Integer or `collections.abc.Container` "
#~ "instance (e.g. list, tuple, etc.).  If"
#~ " an integer, specifies how many "
#~ "training epochs to run before a "
#~ "new validation run is performed, e.g."
#~ " `validation_freq=2` runs validation every "
#~ "2 epochs. If a Container, specifies "
#~ "the epochs on which to run "
#~ "validation, e.g. `validation_freq=[1, 2, 10]`"
#~ " runs validation at the end of "
#~ "the 1st, 2nd, and 10th epochs."
#~ msgstr ""

#~ msgid ""
#~ "Integer. Used for generator or "
#~ "`keras.utils.Sequence` input only. Maximum "
#~ "size for the generator queue.  If "
#~ "unspecified, `max_queue_size` will default to"
#~ " 10."
#~ msgstr ""

#~ msgid "A common pattern is to pass a tf.data.Dataset, generator, or"
#~ msgstr ""

#~ msgid ""
#~ "tf.keras.utils.Sequence to the `x` argument"
#~ " of fit, which will in fact "
#~ "yield not only features (x) but "
#~ "optionally targets (y) and sample "
#~ "weights.  Keras requires that the output"
#~ " of such iterator-likes be "
#~ "unambiguous. The iterator should return "
#~ "a tuple of length 1, 2, or "
#~ "3, where the optional second and "
#~ "third elements will be used for y"
#~ " and sample_weight respectively. Any other"
#~ " type provided will be wrapped in "
#~ "a length one tuple, effectively treating"
#~ " everything as 'x'. When yielding "
#~ "dicts, they should still adhere to "
#~ "the top-level tuple structure. e.g. "
#~ "`({\"x0\": x0, \"x1\": x1}, y)`. Keras"
#~ " will not attempt to separate "
#~ "features, targets, and weights from the"
#~ " keys of a single dict."
#~ msgstr ""

#~ msgid "A notable unsupported data type is the namedtuple. The reason is"
#~ msgstr ""

#~ msgid ""
#~ "that it behaves like both an "
#~ "ordered datatype (tuple) and a mapping"
#~ " datatype (dict). So given a "
#~ "namedtuple of the form:"
#~ msgstr ""

#~ msgid "`namedtuple(\"example_tuple\", [\"y\", \"x\"])`"
#~ msgstr ""

#~ msgid ""
#~ "it is ambiguous whether to reverse "
#~ "the order of the elements when "
#~ "interpreting the value. Even worse is"
#~ " a tuple of the form:"
#~ msgstr ""

#~ msgid "`namedtuple(\"other_tuple\", [\"x\", \"y\", \"z\"])`"
#~ msgstr ""

#~ msgid ""
#~ "where it is unclear if the tuple"
#~ " was intended to be unpacked into "
#~ "x, y, and sample_weight or passed "
#~ "through as a single element to "
#~ "`x`. As a result the data "
#~ "processing code will simply raise a "
#~ "ValueError if it encounters a "
#~ "namedtuple. (Along with instructions to "
#~ "remedy the issue.)"
#~ msgstr ""

#~ msgid "1. If the model was never compiled or,"
#~ msgstr ""

#~ msgid "If the model was never compiled or,"
#~ msgstr ""

#~ msgid ""
#~ "In case of mismatch between the "
#~ "provided input data     and what the "
#~ "model expects or when the input "
#~ "data is empty."
#~ msgstr ""

#~ msgid "Fits the model on data yielded batch-by-batch by a Python generator."
#~ msgstr ""

#~ msgid ""
#~ "`Model.fit` now supports generators, so "
#~ "there is no longer any need to "
#~ "use this endpoint."
#~ msgstr ""

#~ msgid "Creates a layer from its config."
#~ msgstr ""

#~ msgid ""
#~ "This method is the reverse of "
#~ "`get_config`, capable of instantiating the "
#~ "same layer from the config dictionary."
#~ " It does not handle layer "
#~ "connectivity (handled by Network), nor "
#~ "weights (handled by `set_weights`)."
#~ msgstr ""

#~ msgid "A Python dictionary, typically the output of get_config."
#~ msgstr ""

#~ msgid "Returns the config of the `Model`."
#~ msgstr ""

#~ msgid ""
#~ "Config is a Python dictionary "
#~ "(serializable) containing the configuration of"
#~ " an object, which in this case "
#~ "is a `Model`. This allows the "
#~ "`Model` to be be reinstantiated later"
#~ " (without its trained weights) from "
#~ "this configuration."
#~ msgstr ""

#~ msgid ""
#~ "Note that `get_config()` does not "
#~ "guarantee to return a fresh copy "
#~ "of dict every time it is called."
#~ " The callers should make a copy "
#~ "of the returned dict if they want"
#~ " to modify it."
#~ msgstr ""

#~ msgid ""
#~ "Developers of subclassed `Model` are "
#~ "advised to override this method, and "
#~ "continue to update the dict from "
#~ "`super(MyModel, self).get_config()` to provide "
#~ "the proper configuration of this "
#~ "`Model`. The default config will return"
#~ " config dict for init parameters if"
#~ " they are basic types. Raises "
#~ "`NotImplementedError` when in cases where "
#~ "a custom `get_config()` implementation is "
#~ "required for the subclassed model."
#~ msgstr ""

#~ msgid "Python dictionary containing the configuration of this `Model`."
#~ msgstr ""

#~ msgid "Retrieves the input tensor(s) of a layer at a given node."
#~ msgstr ""

#~ msgid ""
#~ "Integer, index of the node from "
#~ "which to retrieve the attribute. E.g."
#~ " `node_index=0` will correspond to the "
#~ "first input node of the layer."
#~ msgstr ""

#~ msgid "A tensor (or list of tensors if the layer has multiple inputs)."
#~ msgstr ""

#~ msgid "If called in Eager mode."
#~ msgstr ""

#~ msgid "Retrieves the input mask tensor(s) of a layer at a given node."
#~ msgstr ""

#~ msgid ""
#~ "Integer, index of the node from "
#~ "which to retrieve the attribute. E.g."
#~ " `node_index=0` will correspond to the "
#~ "first time the layer was called."
#~ msgstr ""

#~ msgid "A mask tensor (or list of tensors if the layer has multiple inputs)."
#~ msgstr ""

#~ msgid "Retrieves the input shape(s) of a layer at a given node."
#~ msgstr ""

#~ msgid ""
#~ "A shape tuple (or list of shape"
#~ " tuples if the layer has multiple "
#~ "inputs)."
#~ msgstr ""

#~ msgid "Retrieves the output tensor(s) of a layer at a given node."
#~ msgstr ""

#~ msgid ""
#~ "Integer, index of the node from "
#~ "which to retrieve the attribute. E.g."
#~ " `node_index=0` will correspond to the "
#~ "first output node of the layer."
#~ msgstr ""

#~ msgid "A tensor (or list of tensors if the layer has multiple outputs)."
#~ msgstr ""

#~ msgid "Retrieves the output mask tensor(s) of a layer at a given node."
#~ msgstr ""

#~ msgid "A mask tensor (or list of tensors if the layer has multiple outputs)."
#~ msgstr ""

#~ msgid "Retrieves the output shape(s) of a layer at a given node."
#~ msgstr ""

#~ msgid ""
#~ "A shape tuple (or list of shape"
#~ " tuples if the layer has multiple "
#~ "outputs)."
#~ msgstr ""

#~ msgid "Retrieve all the variables and their paths for the model."
#~ msgstr ""

#~ msgid ""
#~ "The variable path (string) is a "
#~ "stable key to identify a `tf.Variable`"
#~ " instance owned by the model. It "
#~ "can be used to specify variable-"
#~ "specific configurations (e.g. DTensor, "
#~ "quantization) from a global view."
#~ msgstr ""

#~ msgid ""
#~ "This method returns a dict with "
#~ "weight object paths as keys and "
#~ "the corresponding `tf.Variable` instances as"
#~ " values."
#~ msgstr ""

#~ msgid ""
#~ "Note that if the model is a "
#~ "subclassed model and the weights haven't"
#~ " been initialized, an empty dict will"
#~ " be returned."
#~ msgstr ""

#~ msgid ""
#~ "A dict where keys are variable "
#~ "paths and values are `tf.Variable`  "
#~ "instances."
#~ msgstr ""

#~ msgid "A dict where keys are variable paths and values are `tf.Variable`"
#~ msgstr ""

#~ msgid "instances."
#~ msgstr ""

#~ msgid "```python class SubclassModel(tf.keras.Model):"
#~ msgstr ""

#~ msgid "def __init__(self, name=None):"
#~ msgstr ""

#~ msgid ""
#~ "super().__init__(name=name) self.d1 = "
#~ "tf.keras.layers.Dense(10) self.d2 = "
#~ "tf.keras.layers.Dense(20)"
#~ msgstr ""

#~ msgid "x = self.d1(inputs) return self.d2(x)"
#~ msgstr ""

#~ msgid ""
#~ "model = SubclassModel() model(tf.zeros((10, "
#~ "10))) weight_paths = model.get_weight_paths() "
#~ "# weight_paths: # { #    'd1.kernel':"
#~ " model.d1.kernel, #    'd1.bias': model.d1.bias,"
#~ " #    'd2.kernel': model.d2.kernel, #    "
#~ "'d2.bias': model.d2.bias, # }"
#~ msgstr ""

#~ msgid ""
#~ "# Functional model inputs = "
#~ "tf.keras.Input((10,), batch_size=10) x = "
#~ "tf.keras.layers.Dense(20, name='d1')(inputs) output "
#~ "= tf.keras.layers.Dense(30, name='d2')(x) model "
#~ "= tf.keras.Model(inputs, output) d1 = "
#~ "model.layers[1] d2 = model.layers[2] "
#~ "weight_paths = model.get_weight_paths() # "
#~ "weight_paths: # { #    'd1.kernel': "
#~ "d1.kernel, #    'd1.bias': d1.bias, #    "
#~ "'d2.kernel': d2.kernel, #    'd2.bias': "
#~ "d2.bias, # } ```"
#~ msgstr ""

#~ msgid "Retrieves the weights of the model."
#~ msgstr ""

#~ msgid "A flat list of Numpy arrays."
#~ msgstr ""

#~ msgid "Return Functional API nodes upstream of this layer."
#~ msgstr ""

#~ msgid "Retrieves the input tensor(s) of a layer."
#~ msgstr ""

#~ msgid ""
#~ "Only applicable if the layer has "
#~ "exactly one input, i.e. if it is"
#~ " connected to one incoming layer."
#~ msgstr ""

#~ msgid "If no inbound nodes are found."
#~ msgstr ""

#~ msgid "Retrieves the input mask tensor(s) of a layer."
#~ msgstr ""

#~ msgid ""
#~ "Only applicable if the layer has "
#~ "exactly one inbound node, i.e. if "
#~ "it is connected to one incoming "
#~ "layer."
#~ msgstr ""

#~ msgid "Input mask tensor (potentially None) or list of input mask tensors."
#~ msgstr ""

#~ msgid "if the layer is connected to"
#~ msgstr ""

#~ msgid "Retrieves the input shape(s) of a layer."
#~ msgstr ""

#~ msgid ""
#~ "Only applicable if the layer has "
#~ "exactly one input, i.e. if it is"
#~ " connected to one incoming layer, or"
#~ " if all inputs have the same "
#~ "shape."
#~ msgstr ""

#~ msgid ""
#~ "Input shape, as an integer shape "
#~ "tuple (or list of shape tuples, "
#~ "one tuple per input tensor)."
#~ msgstr ""

#~ msgid "if the layer has no defined input_shape."
#~ msgstr ""

#~ msgid "if called in Eager mode."
#~ msgstr ""

#~ msgid "`InputSpec` instance(s) describing the input format for this layer."
#~ msgstr ""

#~ msgid ""
#~ "When you create a layer subclass, "
#~ "you can set `self.input_spec` to enable"
#~ " the layer to run input compatibility"
#~ " checks when it is called. Consider"
#~ " a `Conv2D` layer: it can only "
#~ "be called on a single input tensor"
#~ " of rank 4. As such, you can"
#~ " set, in `__init__()`:"
#~ msgstr ""

#~ msgid "```python self.input_spec = tf.keras.layers.InputSpec(ndim=4) ```"
#~ msgstr ""

#~ msgid ""
#~ "Now, if you try to call the "
#~ "layer on an input that isn't rank"
#~ " 4 (for instance, an input of "
#~ "shape `(2,)`, it will raise a "
#~ "nicely-formatted error:"
#~ msgstr ""

#~ msgid ""
#~ "``` ValueError: Input 0 of layer "
#~ "conv2d is incompatible with the layer:"
#~ " expected ndim=4, found ndim=1. Full "
#~ "shape received: [2] ```"
#~ msgstr ""

#~ msgid ""
#~ "Input checks that can be specified "
#~ "via `input_spec` include: - Structure "
#~ "(e.g. a single input, a list of"
#~ " 2 inputs, etc) - Shape - Rank"
#~ " (ndim) - Dtype"
#~ msgstr ""

#~ msgid "For more information, see `tf.keras.layers.InputSpec`."
#~ msgstr ""

#~ msgid "A `tf.keras.layers.InputSpec` instance, or nested structure thereof."
#~ msgstr ""

#~ msgid "Specify whether to compile the model with XLA."
#~ msgstr ""

#~ msgid ""
#~ "[XLA](https://www.tensorflow.org/xla) is an "
#~ "optimizing compiler for machine learning. "
#~ "`jit_compile` is not enabled by default."
#~ " Note that `jit_compile=True` may not "
#~ "necessarily work for all models."
#~ msgstr ""

#~ msgid ""
#~ "For more information on supported "
#~ "operations please refer to the [XLA "
#~ "documentation](https://www.tensorflow.org/xla). Also refer"
#~ " to [known XLA "
#~ "issues](https://www.tensorflow.org/xla/known_issues) for "
#~ "more details."
#~ msgstr ""

#~ msgid "Loads all layer weights from a saved files."
#~ msgstr ""

#~ msgid ""
#~ "The saved file could be a "
#~ "SavedModel file, a `.keras` file (v3 "
#~ "saving format), or a file created "
#~ "via `model.save_weights()`."
#~ msgstr ""

#~ msgid ""
#~ "By default, weights are loaded based "
#~ "on the network's topology. This means"
#~ " the architecture should be the same"
#~ " as when the weights were saved. "
#~ "Note that layers that don't have "
#~ "weights are not taken into account "
#~ "in the topological ordering, so adding"
#~ " or removing layers is fine as "
#~ "long as they don't have weights."
#~ msgstr ""

#~ msgid "**Weight loading by name**"
#~ msgstr ""

#~ msgid ""
#~ "If your weights are saved as a "
#~ "`.h5` file created via `model.save_weights()`,"
#~ " you can use the argument "
#~ "`by_name=True`."
#~ msgstr ""

#~ msgid ""
#~ "In this case, weights are loaded "
#~ "into layers only if they share the"
#~ " same name. This is useful for "
#~ "fine-tuning or transfer-learning models "
#~ "where some of the layers have "
#~ "changed."
#~ msgstr ""

#~ msgid ""
#~ "Note that only topological loading "
#~ "(`by_name=False`) is supported when loading"
#~ " weights from the `.keras` v3 format"
#~ " or from the TensorFlow SavedModel "
#~ "format."
#~ msgstr ""

#~ msgid ""
#~ "String, path to the weights file "
#~ "to load. For weight files in "
#~ "TensorFlow format, this is the file "
#~ "prefix (the same as was passed to"
#~ " `save_weights()`). This can also be "
#~ "a path to a SavedModel or a "
#~ "`.keras` file (v3 saving format) saved"
#~ " via `model.save()`."
#~ msgstr ""

#~ msgid ""
#~ "Boolean, whether to load weights by "
#~ "name or by topological order. Only "
#~ "topological loading is supported for "
#~ "weight files in the `.keras` v3 "
#~ "format or in the TensorFlow SavedModel"
#~ " format."
#~ msgstr ""

#~ msgid ""
#~ "Optional `tf.train.CheckpointOptions` object that"
#~ " specifies options for loading weights "
#~ "(only valid for a SavedModel file)."
#~ msgstr ""

#~ msgid "List of losses added using the `add_loss()` API."
#~ msgstr ""

#~ msgid ""
#~ "Variable regularization tensors are created"
#~ " when this property is accessed, so"
#~ " it is eager safe: accessing `losses`"
#~ " under a `tf.GradientTape` will propagate"
#~ " gradients back to the corresponding "
#~ "variables."
#~ msgstr ""

#~ msgid "A list of tensors."
#~ msgstr ""

#~ msgid "Creates a function that executes one step of inference."
#~ msgstr ""

#~ msgid ""
#~ "This method can be overridden to "
#~ "support custom inference logic. This "
#~ "method is called by `Model.predict` and"
#~ " `Model.predict_on_batch`."
#~ msgstr ""

#~ msgid ""
#~ "Typically, this method directly controls "
#~ "`tf.function` and `tf.distribute.Strategy` settings,"
#~ " and delegates the actual evaluation "
#~ "logic to `Model.predict_step`."
#~ msgstr ""

#~ msgid ""
#~ "This function is cached the first "
#~ "time `Model.predict` or `Model.predict_on_batch` "
#~ "is called. The cache is cleared "
#~ "whenever `Model.compile` is called. You "
#~ "can skip the cache and generate "
#~ "again the function with `force=True`."
#~ msgstr ""

#~ msgid ""
#~ "Whether to regenerate the predict "
#~ "function and skip the cached function"
#~ " if available."
#~ msgstr ""

#~ msgid ""
#~ "Function. The function created by this"
#~ " method should accept a `tf.data.Iterator`,"
#~ " and return the outputs of the "
#~ "`Model`."
#~ msgstr ""

#~ msgid "Creates a function that executes one step of evaluation."
#~ msgstr ""

#~ msgid ""
#~ "This method can be overridden to "
#~ "support custom evaluation logic. This "
#~ "method is called by `Model.evaluate` and"
#~ " `Model.test_on_batch`."
#~ msgstr ""

#~ msgid ""
#~ "Typically, this method directly controls "
#~ "`tf.function` and `tf.distribute.Strategy` settings,"
#~ " and delegates the actual evaluation "
#~ "logic to `Model.test_step`."
#~ msgstr ""

#~ msgid ""
#~ "This function is cached the first "
#~ "time `Model.evaluate` or `Model.test_on_batch` "
#~ "is called. The cache is cleared "
#~ "whenever `Model.compile` is called. You "
#~ "can skip the cache and generate "
#~ "again the function with `force=True`."
#~ msgstr ""

#~ msgid ""
#~ "Whether to regenerate the test function"
#~ " and skip the cached function if "
#~ "available."
#~ msgstr ""

#~ msgid ""
#~ "Function. The function created by this"
#~ " method should accept a `tf.data.Iterator`,"
#~ " and return a `dict` containing "
#~ "values that will be passed to "
#~ "`tf.keras.Callbacks.on_test_batch_end`."
#~ msgstr ""

#~ msgid "Creates a function that executes one step of training."
#~ msgstr ""

#~ msgid ""
#~ "This method can be overridden to "
#~ "support custom training logic. This "
#~ "method is called by `Model.fit` and "
#~ "`Model.train_on_batch`."
#~ msgstr ""

#~ msgid ""
#~ "Typically, this method directly controls "
#~ "`tf.function` and `tf.distribute.Strategy` settings,"
#~ " and delegates the actual training "
#~ "logic to `Model.train_step`."
#~ msgstr ""

#~ msgid ""
#~ "This function is cached the first "
#~ "time `Model.fit` or `Model.train_on_batch` is"
#~ " called. The cache is cleared "
#~ "whenever `Model.compile` is called. You "
#~ "can skip the cache and generate "
#~ "again the function with `force=True`."
#~ msgstr ""

#~ msgid ""
#~ "Whether to regenerate the train function"
#~ " and skip the cached function if "
#~ "available."
#~ msgstr ""

#~ msgid ""
#~ "Function. The function created by this"
#~ " method should accept a `tf.data.Iterator`,"
#~ " and return a `dict` containing "
#~ "values that will be passed to "
#~ "`tf.keras.Callbacks.on_train_batch_end`, such as "
#~ "`{'loss': 0.2, 'accuracy': 0.7}`."
#~ msgstr ""

#~ msgid "Return metrics added using `compile()` or `add_metric()`."
#~ msgstr ""

#~ msgid ""
#~ "Note: Metrics passed to `compile()` are"
#~ " available only after a `keras.Model` "
#~ "has been trained/evaluated on actual "
#~ "data."
#~ msgstr ""

#~ msgid "Returns the model's display labels for all outputs."
#~ msgstr ""

#~ msgid ""
#~ "Note: `metrics_names` are available only "
#~ "after a `keras.Model` has been "
#~ "trained/evaluated on actual data."
#~ msgstr ""

#~ msgid "Name of the layer (string), set in the constructor."
#~ msgstr ""

#~ msgid "Returns a `tf.name_scope` instance for this class."
#~ msgstr ""

#~ msgid ""
#~ "Sequence of non-trainable variables "
#~ "owned by this module and its "
#~ "submodules."
#~ msgstr ""

#~ msgid ""
#~ "Note: this method uses reflection to "
#~ "find variables on the current instance"
#~ " and submodules. For performance reasons"
#~ " you may wish to cache the "
#~ "result of calling this method if "
#~ "you don't expect the return value "
#~ "to change."
#~ msgstr ""

#~ msgid ""
#~ "A sequence of variables for the "
#~ "current module (sorted by attribute "
#~ "name) followed by variables from all "
#~ "submodules recursively (breadth first)."
#~ msgstr ""

#~ msgid "List of all non-trainable weights tracked by this layer."
#~ msgstr ""

#~ msgid ""
#~ "Non-trainable weights are *not* updated"
#~ " during training. They are expected "
#~ "to be updated manually in `call()`."
#~ msgstr ""

#~ msgid "A list of non-trainable variables."
#~ msgstr ""

#~ msgid "Return Functional API nodes downstream of this layer."
#~ msgstr ""

#~ msgid ""
#~ "Only applicable if the layer has "
#~ "exactly one output, i.e. if it is"
#~ " connected to one incoming layer."
#~ msgstr ""

#~ msgid "if the layer is connected to more than one incoming     layers."
#~ msgstr ""

#~ msgid "Retrieves the output mask tensor(s) of a layer."
#~ msgstr ""

#~ msgid "Output mask tensor (potentially None) or list of output mask tensors."
#~ msgstr ""

#~ msgid "Retrieves the output shape(s) of a layer."
#~ msgstr ""

#~ msgid ""
#~ "Only applicable if the layer has "
#~ "one output, or if all outputs have"
#~ " the same shape."
#~ msgstr ""

#~ msgid ""
#~ "Output shape, as an integer shape "
#~ "tuple (or list of shape tuples, "
#~ "one tuple per output tensor)."
#~ msgstr ""

#~ msgid "if the layer has no defined output shape."
#~ msgstr ""

#~ msgid ""
#~ "For small numbers of inputs that "
#~ "fit in one batch, directly use "
#~ "`__call__()` for faster execution, e.g., "
#~ "`model(x)`, or `model(x, training=False)` if"
#~ " you have layers such as "
#~ "`tf.keras.layers.BatchNormalization` that behave "
#~ "differently during inference. You may "
#~ "pair the individual model call with "
#~ "a `tf.function` for additional performance "
#~ "inside your inner loop. If you "
#~ "need access to numpy array values "
#~ "instead of tensors after your model "
#~ "call, you can use `tensor.numpy()` to"
#~ " get the numpy array value of "
#~ "an eager tensor."
#~ msgstr ""

#~ msgid ""
#~ "Also, note the fact that test loss"
#~ " is not affected by regularization "
#~ "layers like noise and dropout."
#~ msgstr ""

#~ msgid ""
#~ "Input samples. It could be: - A"
#~ " Numpy array (or array-like), or "
#~ "a list of arrays   (in case the"
#~ " model has multiple inputs). - A "
#~ "TensorFlow tensor, or a list of "
#~ "tensors   (in case the model has "
#~ "multiple inputs). - A `tf.data` dataset."
#~ " - A generator or `keras.utils.Sequence`"
#~ " instance. A more detailed description "
#~ "of unpacking behavior for iterator types"
#~ " (Dataset, generator, Sequence) is given"
#~ " in the `Unpacking behavior for "
#~ "iterator-like inputs` section of "
#~ "`Model.fit`."
#~ msgstr ""

#~ msgid ""
#~ "Input samples. It could be: - A"
#~ " Numpy array (or array-like), or "
#~ "a list of arrays"
#~ msgstr ""

#~ msgid "A `tf.data` dataset."
#~ msgstr ""

#~ msgid "A generator or `keras.utils.Sequence` instance."
#~ msgstr ""

#~ msgid ""
#~ "Integer or `None`. Number of samples "
#~ "per batch. If unspecified, `batch_size` "
#~ "will default to 32. Do not specify"
#~ " the `batch_size` if your data is "
#~ "in the form of dataset, generators, "
#~ "or `keras.utils.Sequence` instances (since "
#~ "they generate batches)."
#~ msgstr ""

#~ msgid ""
#~ "Total number of steps (batches of "
#~ "samples) before declaring the prediction "
#~ "round finished. Ignored with the default"
#~ " value of `None`. If x is a "
#~ "`tf.data` dataset and `steps` is None,"
#~ " `predict()` will run until the input"
#~ " dataset is exhausted."
#~ msgstr ""

#~ msgid ""
#~ "List of `keras.callbacks.Callback` instances. "
#~ "List of callbacks to apply during "
#~ "prediction. See [callbacks]( "
#~ "https://www.tensorflow.org/api_docs/python/tf/keras/callbacks)."
#~ msgstr ""

#~ msgid ""
#~ "See the discussion of `Unpacking "
#~ "behavior for iterator-like inputs` for"
#~ " `Model.fit`. Note that Model.predict uses"
#~ " the same interpretation rules as "
#~ "`Model.fit` and `Model.evaluate`, so inputs"
#~ " must be unambiguous for all three"
#~ " methods."
#~ msgstr ""

#~ msgid "Numpy array(s) of predictions."
#~ msgstr ""

#~ msgid "If `model.predict` is wrapped in a `tf.function`."
#~ msgstr ""

#~ msgid ""
#~ "In case of mismatch between the "
#~ "provided     input data and the model's"
#~ " expectations,     or in case a "
#~ "stateful model receives a number of "
#~ "samples     that is not a multiple "
#~ "of the batch size."
#~ msgstr ""

#~ msgid "Generates predictions for the input samples from a data generator."
#~ msgstr ""

#~ msgid ""
#~ "`Model.predict` now supports generators, so"
#~ " there is no longer any need to"
#~ " use this endpoint."
#~ msgstr ""

#~ msgid ""
#~ "Input data. It could be: - A "
#~ "Numpy array (or array-like), or a"
#~ " list of arrays (in case the     "
#~ "model has multiple inputs). - A "
#~ "TensorFlow tensor, or a list of "
#~ "tensors (in case the model has     "
#~ "multiple inputs)."
#~ msgstr ""

#~ msgid ""
#~ "Input data. It could be: - A "
#~ "Numpy array (or array-like), or a"
#~ " list of arrays (in case the"
#~ msgstr ""

#~ msgid "model has multiple inputs)."
#~ msgstr ""

#~ msgid "A TensorFlow tensor, or a list of tensors (in case the model has"
#~ msgstr ""

#~ msgid "multiple inputs)."
#~ msgstr ""

#~ msgid "If `model.predict_on_batch` is wrapped in a     `tf.function`."
#~ msgstr ""

#~ msgid "The logic for one inference step."
#~ msgstr ""

#~ msgid ""
#~ "This method can be overridden to "
#~ "support custom inference logic. This "
#~ "method is called by "
#~ "`Model.make_predict_function`."
#~ msgstr ""

#~ msgid ""
#~ "This method should contain the "
#~ "mathematical logic for one step of "
#~ "inference.  This typically includes the "
#~ "forward pass."
#~ msgstr ""

#~ msgid ""
#~ "Configuration details for *how* this "
#~ "logic is run (e.g. `tf.function` and "
#~ "`tf.distribute.Strategy` settings), should be "
#~ "left to `Model.make_predict_function`, which "
#~ "can also be overridden."
#~ msgstr ""

#~ msgid "A nested structure of `Tensor`s."
#~ msgstr ""

#~ msgid ""
#~ "The result of one inference step, "
#~ "typically the output of calling the "
#~ "`Model` on data."
#~ msgstr ""

#~ msgid "Resets the state of all the metrics in the model."
#~ msgstr ""

#~ msgid "Settable attribute indicating whether the model should run eagerly."
#~ msgstr ""

#~ msgid ""
#~ "Running eagerly means that your model"
#~ " will be run step by step, like"
#~ " Python code. Your model might run"
#~ " slower, but it should become easier"
#~ " for you to debug it by "
#~ "stepping into individual layer calls."
#~ msgstr ""

#~ msgid ""
#~ "By default, we will attempt to "
#~ "compile your model to a static "
#~ "graph to deliver the best execution "
#~ "performance."
#~ msgstr ""

#~ msgid "Boolean, whether the model should run eagerly."
#~ msgstr ""

#~ msgid "Saves a model as a TensorFlow SavedModel or HDF5 file."
#~ msgstr ""

#~ msgid "See the [Serialization and Saving guide]("
#~ msgstr ""

#~ msgid "https://keras.io/guides/serialization_and_saving/) for details."
#~ msgstr ""

#~ msgid "Keras model instance to be saved."
#~ msgstr ""

#~ msgid "`str` or `pathlib.Path` object. Path where to save the model."
#~ msgstr ""

#~ msgid ""
#~ "Either `\"keras\"`, `\"tf\"`, `\"h5\"`, "
#~ "indicating whether to save the model "
#~ "in the native Keras format (`.keras`),"
#~ " in the TensorFlow SavedModel format "
#~ "(referred to as \"SavedModel\" below), "
#~ "or in the legacy HDF5 format "
#~ "(`.h5`). Defaults to `\"tf\"` in TF "
#~ "2.X, and `\"h5\"` in TF 1.X."
#~ msgstr ""

#~ msgid "SavedModel format arguments:"
#~ msgstr ""

#~ msgid "include_optimizer: Only applied to SavedModel and legacy HDF5"
#~ msgstr ""

#~ msgid "formats. If False, do not save the optimizer state. Defaults to `True`."
#~ msgstr ""

#~ msgid "signatures: Only applies to SavedModel format. Signatures to save"
#~ msgstr ""

#~ msgid ""
#~ "with the SavedModel. See the "
#~ "`signatures` argument in `tf.saved_model.save` "
#~ "for details."
#~ msgstr ""

#~ msgid "options: Only applies to SavedModel format."
#~ msgstr ""

#~ msgid ""
#~ "`tf.saved_model.SaveOptions` object that specifies"
#~ " SavedModel saving options."
#~ msgstr ""

#~ msgid "save_traces: Only applies to SavedModel format. When enabled, the"
#~ msgstr ""

#~ msgid ""
#~ "SavedModel will store the function "
#~ "traces for each layer. This can be"
#~ " disabled, so that only the configs"
#~ " of each layer are stored. Defaults"
#~ " to `True`. Disabling this will "
#~ "decrease serialization time and reduce "
#~ "file size, but it requires that "
#~ "all custom layers/models implement a "
#~ "`get_config()` method."
#~ msgstr ""

#~ msgid "```python model = tf.keras.Sequential(["
#~ msgstr ""

#~ msgid "tf.keras.layers.Dense(5, input_shape=(3,)), tf.keras.layers.Softmax()])"
#~ msgstr ""

#~ msgid ""
#~ "model.save(\"model.keras\") loaded_model = "
#~ "tf.keras.models.load_model(\"model.keras\") x = "
#~ "tf.random.uniform((10, 3)) assert "
#~ "np.allclose(model.predict(x), loaded_model.predict(x)) ```"
#~ msgstr ""

#~ msgid ""
#~ "Note that `model.save()` is an alias "
#~ "for `tf.keras.models.save_model()`."
#~ msgstr ""

#~ msgid "Returns the `tf.TensorSpec` of call args as a tuple `(args, kwargs)`."
#~ msgstr ""

#~ msgid ""
#~ "This value is automatically defined "
#~ "after calling the model for the "
#~ "first time. Afterwards, you can use "
#~ "it when exporting the model for "
#~ "serving:"
#~ msgstr ""

#~ msgid "```python model = tf.keras.Model(...)"
#~ msgstr ""

#~ msgid "@tf.function def serve(*args, **kwargs):"
#~ msgstr ""

#~ msgid ""
#~ "outputs = model(*args, **kwargs) # Apply"
#~ " postprocessing steps, or add additional"
#~ " outputs. ... return outputs"
#~ msgstr ""

#~ msgid ""
#~ "# arg_specs is `[tf.TensorSpec(...), ...]`."
#~ " kwarg_specs, in this # example, is"
#~ " an empty dict since functional "
#~ "models do not use keyword # "
#~ "arguments. arg_specs, kwarg_specs = "
#~ "model.save_spec()"
#~ msgstr ""

#~ msgid "model.save(path, signatures={"
#~ msgstr ""

#~ msgid "'serving_default': serve.get_concrete_function(*arg_specs,"
#~ msgstr ""

#~ msgid "**kwarg_specs)"
#~ msgstr ""

#~ msgid "})"
#~ msgstr ""

#~ msgid "param dynamic_batch"
#~ msgstr ""

#~ msgid ""
#~ "Whether to set the batch sizes of"
#~ " all the returned `tf.TensorSpec` to "
#~ "`None`. (Note that when defining "
#~ "functional or Sequential models with "
#~ "`tf.keras.Input([...], batch_size=X)`, the batch "
#~ "size will always be preserved). Defaults"
#~ " to `True`."
#~ msgstr ""

#~ msgid "returns"
#~ msgstr ""

#~ msgid ""
#~ "If the model inputs are defined, "
#~ "returns a tuple `(args, kwargs)`. All"
#~ " elements in `args` and `kwargs` are"
#~ " `tf.TensorSpec`. If the model inputs "
#~ "are not defined, returns `None`. The "
#~ "model inputs are automatically set when"
#~ " calling the model, `model.fit`, "
#~ "`model.evaluate` or `model.predict`."
#~ msgstr ""

#~ msgid "Saves all layer weights."
#~ msgstr ""

#~ msgid ""
#~ "Either saves in HDF5 or in "
#~ "TensorFlow format based on the "
#~ "`save_format` argument."
#~ msgstr ""

#~ msgid "When saving in HDF5 format, the weight file has:"
#~ msgstr ""

#~ msgid "`layer_names` (attribute), a list of strings"
#~ msgstr ""

#~ msgid "(ordered names of model layers)."
#~ msgstr ""

#~ msgid "For every layer, a `group` named `layer.name`"
#~ msgstr ""

#~ msgid "For every such layer group, a group attribute `weight_names`,"
#~ msgstr ""

#~ msgid "a list of strings (ordered names of weights tensor of the layer)."
#~ msgstr ""

#~ msgid "For every weight in the layer, a dataset"
#~ msgstr ""

#~ msgid "storing the weight value, named after the weight tensor."
#~ msgstr ""

#~ msgid ""
#~ "When saving in TensorFlow format, all"
#~ " objects referenced by the network "
#~ "are saved in the same format as"
#~ " `tf.train.Checkpoint`, including any `Layer` "
#~ "instances or `Optimizer` instances assigned"
#~ " to object attributes. For networks "
#~ "constructed from inputs and outputs "
#~ "using `tf.keras.Model(inputs, outputs)`, `Layer` "
#~ "instances used by the network are "
#~ "tracked/saved automatically. For user-defined"
#~ " classes which inherit from "
#~ "`tf.keras.Model`, `Layer` instances must be"
#~ " assigned to object attributes, typically"
#~ " in the constructor. See the "
#~ "documentation of `tf.train.Checkpoint` and "
#~ "`tf.keras.Model` for details."
#~ msgstr ""

#~ msgid ""
#~ "While the formats are the same, do"
#~ " not mix `save_weights` and "
#~ "`tf.train.Checkpoint`. Checkpoints saved by "
#~ "`Model.save_weights` should be loaded using"
#~ " `Model.load_weights`. Checkpoints saved using"
#~ " `tf.train.Checkpoint.save` should be restored"
#~ " using the corresponding "
#~ "`tf.train.Checkpoint.restore`. Prefer "
#~ "`tf.train.Checkpoint` over `save_weights` for "
#~ "training checkpoints."
#~ msgstr ""

#~ msgid ""
#~ "The TensorFlow format matches objects "
#~ "and variables by starting at a "
#~ "root object, `self` for `save_weights`, "
#~ "and greedily matching attribute names. "
#~ "For `Model.save` this is the `Model`,"
#~ " and for `Checkpoint.save` this is "
#~ "the `Checkpoint` even if the "
#~ "`Checkpoint` has a model attached. This"
#~ " means saving a `tf.keras.Model` using "
#~ "`save_weights` and loading into a "
#~ "`tf.train.Checkpoint` with a `Model` attached"
#~ " (or vice versa) will not match "
#~ "the `Model`'s variables. See the [guide"
#~ " to training checkpoints]( "
#~ "https://www.tensorflow.org/guide/checkpoint) for details"
#~ " on the TensorFlow format."
#~ msgstr ""

#~ msgid ""
#~ "String or PathLike, path to the "
#~ "file to save the weights to. When"
#~ " saving in TensorFlow format, this is"
#~ " the prefix used for checkpoint files"
#~ " (multiple files are generated). Note "
#~ "that the '.h5' suffix causes weights "
#~ "to be saved in HDF5 format."
#~ msgstr ""

#~ msgid ""
#~ "Whether to silently overwrite any "
#~ "existing file at the target location,"
#~ " or provide the user with a "
#~ "manual prompt."
#~ msgstr ""

#~ msgid ""
#~ "Either 'tf' or 'h5'. A `filepath` "
#~ "ending in '.h5' or '.keras' will "
#~ "default to HDF5 if `save_format` is "
#~ "`None`. Otherwise, `None` becomes 'tf'. "
#~ "Defaults to `None`."
#~ msgstr ""

#~ msgid ""
#~ "Optional `tf.train.CheckpointOptions` object that"
#~ " specifies options for saving weights."
#~ msgstr ""

#~ msgid "If `h5py` is not available when attempting to save in     HDF5 format."
#~ msgstr ""

#~ msgid "Sets the weights of the layer, from NumPy arrays."
#~ msgstr ""

#~ msgid ""
#~ "The weights of a layer represent "
#~ "the state of the layer. This "
#~ "function sets the weight values from "
#~ "numpy arrays. The weight values should"
#~ " be passed in the order they "
#~ "are created by the layer. Note "
#~ "that the layer's weights must be "
#~ "instantiated before calling this function, "
#~ "by calling the layer."
#~ msgstr ""

#~ msgid ""
#~ "For example, a `Dense` layer returns "
#~ "a list of two values: the kernel"
#~ " matrix and the bias vector. These"
#~ " can be used to set the weights"
#~ " of another `Dense` layer:"
#~ msgstr ""

#~ msgid ""
#~ "a list of NumPy arrays. The number"
#~ " of arrays and their shape must "
#~ "match number of the dimensions of "
#~ "the weights of the layer (i.e. it"
#~ " should match the output of "
#~ "`get_weights`)."
#~ msgstr ""

#~ msgid ""
#~ "If the provided weights list does "
#~ "not match the     layer's specifications."
#~ msgstr ""

#~ msgid "Deprecated, do NOT use!"
#~ msgstr ""

#~ msgid "Returns the `updates` from all layers that are stateful."
#~ msgstr ""

#~ msgid ""
#~ "This is useful for separating training"
#~ " updates and state updates, e.g. when"
#~ " we need to update a layer's "
#~ "internal state during prediction."
#~ msgstr ""

#~ msgid "A list of update ops."
#~ msgstr ""

#~ msgid "Sequence of all sub-modules."
#~ msgstr ""

#~ msgid ""
#~ "Submodules are modules which are "
#~ "properties of this module, or found "
#~ "as properties of modules which are "
#~ "properties of this module (and so "
#~ "on)."
#~ msgstr ""

#~ msgid "A sequence of all submodules."
#~ msgstr ""

#~ msgid ""
#~ "a list or tuple of 2 strings, "
#~ "which is the starting layer name "
#~ "and ending layer name (both inclusive)"
#~ " indicating the range of layers to"
#~ " be printed in summary. It also "
#~ "accepts regex patterns instead of exact"
#~ " name. In such case, start predicate"
#~ " will be the first element it "
#~ "matches to `layer_range[0]` and the end"
#~ " predicate will be the last element"
#~ " it matches to `layer_range[1]`. By "
#~ "default `None` which considers all "
#~ "layers of model."
#~ msgstr ""

#~ msgid ""
#~ "Input data. It could be: - A "
#~ "Numpy array (or array-like), or a"
#~ " list of arrays (in case the     "
#~ "model has multiple inputs). - A "
#~ "TensorFlow tensor, or a list of "
#~ "tensors (in case the model has     "
#~ "multiple inputs). - A dict mapping "
#~ "input names to the corresponding "
#~ "array/tensors,     if the model has "
#~ "named inputs."
#~ msgstr ""

#~ msgid "A dict mapping input names to the corresponding array/tensors,"
#~ msgstr ""

#~ msgid "if the model has named inputs."
#~ msgstr ""

#~ msgid ""
#~ "Target data. Like the input data "
#~ "`x`, it could be either Numpy "
#~ "array(s) or TensorFlow tensor(s). It "
#~ "should be consistent with `x` (you "
#~ "cannot have Numpy inputs and tensor "
#~ "targets, or inversely)."
#~ msgstr ""

#~ msgid ""
#~ "Optional array of the same length "
#~ "as x, containing weights to apply "
#~ "to the model's loss for each "
#~ "sample. In the case of temporal "
#~ "data, you can pass a 2D array "
#~ "with shape (samples, sequence_length), to "
#~ "apply a different weight to every "
#~ "timestep of every sample."
#~ msgstr ""

#~ msgid ""
#~ "If `True`, the metrics returned will "
#~ "be only for this batch. If "
#~ "`False`, the metrics will be statefully"
#~ " accumulated across batches."
#~ msgstr ""

#~ msgid "If `model.test_on_batch` is wrapped in a     `tf.function`."
#~ msgstr ""

#~ msgid "The logic for one evaluation step."
#~ msgstr ""

#~ msgid ""
#~ "This method can be overridden to "
#~ "support custom evaluation logic. This "
#~ "method is called by "
#~ "`Model.make_test_function`."
#~ msgstr ""

#~ msgid ""
#~ "This function should contain the "
#~ "mathematical logic for one step of "
#~ "evaluation. This typically includes the "
#~ "forward pass, loss calculation, and "
#~ "metrics updates."
#~ msgstr ""

#~ msgid ""
#~ "Configuration details for *how* this "
#~ "logic is run (e.g. `tf.function` and "
#~ "`tf.distribute.Strategy` settings), should be "
#~ "left to `Model.make_test_function`, which can"
#~ " also be overridden."
#~ msgstr ""

#~ msgid ""
#~ "A `dict` containing values that will "
#~ "be passed to "
#~ "`tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically,"
#~ " the values of the `Model`'s metrics"
#~ " are returned."
#~ msgstr ""

#~ msgid ""
#~ "To load a network from a JSON "
#~ "save file, use "
#~ "`keras.models.model_from_json(json_string, custom_objects={})`."
#~ msgstr ""

#~ msgid "Additional keyword arguments to be passed to *`json.dumps()`."
#~ msgstr ""

#~ msgid "Returns a yaml string containing the network configuration."
#~ msgstr ""

#~ msgid ""
#~ "Note: Since TF 2.6, this method is"
#~ " no longer supported and will raise"
#~ " a RuntimeError."
#~ msgstr ""

#~ msgid ""
#~ "To load a network from a yaml "
#~ "save file, use "
#~ "`keras.models.model_from_yaml(yaml_string, custom_objects={})`."
#~ msgstr ""

#~ msgid ""
#~ "`custom_objects` should be a dictionary "
#~ "mapping the names of custom losses "
#~ "/ layers / etc to the "
#~ "corresponding functions / classes."
#~ msgstr ""

#~ msgid "Additional keyword arguments to be passed to `yaml.dump()`."
#~ msgstr ""

#~ msgid "A YAML string."
#~ msgstr ""

#~ msgid "announces that the method poses a security risk"
#~ msgstr ""

#~ msgid ""
#~ "Input data. It could be: - A "
#~ "Numpy array (or array-like), or a"
#~ " list of arrays     (in case the "
#~ "model has multiple inputs). - A "
#~ "TensorFlow tensor, or a list of "
#~ "tensors     (in case the model has "
#~ "multiple inputs). - A dict mapping "
#~ "input names to the corresponding "
#~ "array/tensors,     if the model has "
#~ "named inputs."
#~ msgstr ""

#~ msgid "A TensorFlow tensor, or a list of tensors"
#~ msgstr ""

#~ msgid ""
#~ "Target data. Like the input data "
#~ "`x`, it could be either Numpy "
#~ "array(s) or TensorFlow tensor(s)."
#~ msgstr ""

#~ msgid ""
#~ "Optional dictionary mapping class indices "
#~ "(integers) to a weight (float) to "
#~ "apply to the model's loss for the"
#~ " samples from this class during "
#~ "training. This can be useful to "
#~ "tell the model to \"pay more "
#~ "attention\" to samples from an under-"
#~ "represented class. When `class_weight` is "
#~ "specified and targets have a rank "
#~ "of 2 or greater, either `y` must"
#~ " be one-hot encoded, or an "
#~ "explicit final dimension of `1` must "
#~ "be included for sparse class labels."
#~ msgstr ""

#~ msgid ""
#~ "Scalar training loss (if the model "
#~ "has a single output and no "
#~ "metrics) or list of scalars (if "
#~ "the model has multiple outputs and/or"
#~ " metrics). The attribute `model.metrics_names`"
#~ " will give you the display labels "
#~ "for the scalar outputs."
#~ msgstr ""

#~ msgid "If `model.train_on_batch` is wrapped in a `tf.function`."
#~ msgstr ""

#~ msgid "The logic for one training step."
#~ msgstr ""

#~ msgid ""
#~ "This method can be overridden to "
#~ "support custom training logic. For "
#~ "concrete examples of how to override "
#~ "this method see [Customizing what "
#~ "happens in fit]( "
#~ "https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit)."
#~ " This method is called by "
#~ "`Model.make_train_function`."
#~ msgstr ""

#~ msgid ""
#~ "This method should contain the "
#~ "mathematical logic for one step of "
#~ "training.  This typically includes the "
#~ "forward pass, loss calculation, "
#~ "backpropagation, and metric updates."
#~ msgstr ""

#~ msgid ""
#~ "Configuration details for *how* this "
#~ "logic is run (e.g. `tf.function` and "
#~ "`tf.distribute.Strategy` settings), should be "
#~ "left to `Model.make_train_function`, which can"
#~ " also be overridden."
#~ msgstr ""

#~ msgid ""
#~ "A `dict` containing values that will "
#~ "be passed to "
#~ "`tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically,"
#~ " the values of the `Model`'s metrics"
#~ " are returned. Example: `{'loss': 0.2, "
#~ "'accuracy': 0.7}`."
#~ msgstr ""

#~ msgid ""
#~ "Sequence of trainable variables owned by"
#~ " this module and its submodules."
#~ msgstr ""

#~ msgid "List of all trainable weights tracked by this layer."
#~ msgstr ""

#~ msgid "Trainable weights are updated via gradient descent during training."
#~ msgstr ""

#~ msgid "A list of trainable variables."
#~ msgstr ""

#~ msgid "Alias of `Layer.dtype`, the dtype of the weights."
#~ msgstr ""

#~ msgid "Returns the list of all layer variables/weights."
#~ msgstr ""

#~ msgid "Alias of `self.weights`."
#~ msgstr ""

#~ msgid ""
#~ "Note: This will not track the "
#~ "weights of nested `tf.Modules` that are"
#~ " not themselves Keras layers."
#~ msgstr ""

#~ msgid "A list of variables."
#~ msgstr ""

#~ msgid "Decorator to automatically enter the module name scope."
#~ msgstr ""

#~ msgid ""
#~ "Using the above module would produce "
#~ "`tf.Variable`s and `tf.Tensor`s whose names"
#~ " included the module name:"
#~ msgstr ""

#~ msgid "The method to wrap."
#~ msgstr ""

#~ msgid ""
#~ "The original method wrapped such that"
#~ " it enters the module's name scope."
#~ msgstr ""

#~ msgid "Bases: :py:class:`~keras.src.engine.base_layer.Layer`"
#~ msgstr ""

#~ msgid "Creates the variables of the layer (for subclass implementers)."
#~ msgstr ""

#~ msgid ""
#~ "Instance of `TensorShape`, or list of"
#~ " instances of `TensorShape` if the "
#~ "layer expects a list of inputs "
#~ "(one instance per input)."
#~ msgstr ""

#~ msgid ""
#~ "The `call()` method may not create "
#~ "state (except in its first invocation,"
#~ " wrapping the creation of variables "
#~ "or other resources in `tf.init_scope()`).  "
#~ "It is recommended to create state, "
#~ "including `tf.Variable` instances and nested"
#~ " `Layer` instances,"
#~ msgstr ""

#~ msgid "in `__init__()`, or in the `build()` method that is"
#~ msgstr ""

#~ msgid "called automatically before `call()` executes for the first time."
#~ msgstr ""

#~ msgid ""
#~ "Input tensor, or dict/list/tuple of "
#~ "input tensors. The first positional "
#~ "`inputs` argument is subject to special"
#~ " rules: - `inputs` must be explicitly"
#~ " passed. A layer cannot have zero"
#~ "   arguments, and `inputs` cannot be "
#~ "provided via the default value   of "
#~ "a keyword argument. - NumPy array "
#~ "or Python scalar values in `inputs` "
#~ "get cast as   tensors. - Keras "
#~ "mask metadata is only collected from "
#~ "`inputs`. - Layers are built "
#~ "(`build(input_shape)` method)   using shape "
#~ "info from `inputs` only. - `input_spec`"
#~ " compatibility is only checked against "
#~ "`inputs`. - Mixed precision input "
#~ "casting is only applied to `inputs`."
#~ "   If a layer has tensor arguments"
#~ " in `*args` or `**kwargs`, their   "
#~ "casting behavior in mixed precision "
#~ "should be handled manually. - The "
#~ "SavedModel input specification is generated"
#~ " using `inputs`   only. - Integration "
#~ "with various ecosystem packages like "
#~ "TFMOT, TFLite,   TF.js, etc is only "
#~ "supported for `inputs` and not for "
#~ "tensors in   positional and keyword "
#~ "arguments."
#~ msgstr ""

#~ msgid ""
#~ "Input tensor, or dict/list/tuple of "
#~ "input tensors. The first positional "
#~ "`inputs` argument is subject to special"
#~ " rules: - `inputs` must be explicitly"
#~ " passed. A layer cannot have zero"
#~ msgstr ""

#~ msgid ""
#~ "Additional positional arguments. May contain"
#~ " tensors, although this is not "
#~ "recommended, for the reasons above."
#~ msgstr ""

#~ msgid ""
#~ "Additional keyword arguments. May contain "
#~ "tensors, although this is not "
#~ "recommended, for the reasons above. The"
#~ " following optional keyword arguments are"
#~ " reserved: - `training`: Boolean scalar "
#~ "tensor of Python boolean indicating   "
#~ "whether the `call` is meant for "
#~ "training or inference. - `mask`: Boolean"
#~ " input mask. If the layer's `call()`"
#~ " method takes a   `mask` argument, "
#~ "its default value will be set to"
#~ " the mask   generated for `inputs` by"
#~ " the previous layer (if `input` did"
#~ " come   from a layer that generated"
#~ " a corresponding mask, i.e. if it "
#~ "came   from a Keras layer with "
#~ "masking support)."
#~ msgstr ""

#~ msgid ""
#~ "Additional keyword arguments. May contain "
#~ "tensors, although this is not "
#~ "recommended, for the reasons above. The"
#~ " following optional keyword arguments are"
#~ " reserved: - `training`: Boolean scalar "
#~ "tensor of Python boolean indicating"
#~ msgstr ""

#~ msgid "Returns the config of the layer."
#~ msgstr ""

#~ msgid ""
#~ "A layer config is a Python "
#~ "dictionary (serializable) containing the "
#~ "configuration of a layer. The same "
#~ "layer can be reinstantiated later "
#~ "(without its trained weights) from this"
#~ " configuration."
#~ msgstr ""

#~ msgid ""
#~ "The config of a layer does not "
#~ "include connectivity information, nor the "
#~ "layer class name. These are handled "
#~ "by `Network` (one layer of abstraction"
#~ " above)."
#~ msgstr ""

#~ msgid "Python dictionary."
#~ msgstr ""

#~ msgid "Returns the current weights of the layer, as NumPy arrays."
#~ msgstr ""

#~ msgid ""
#~ "The weights of a layer represent "
#~ "the state of the layer. This "
#~ "function returns both trainable and "
#~ "non-trainable weight values associated with"
#~ " this layer as a list of NumPy"
#~ " arrays, which can in turn be "
#~ "used to load state into similarly "
#~ "parameterized layers."
#~ msgstr ""

#~ msgid "Weights values as a list of NumPy arrays."
#~ msgstr ""

#~ msgid "List of metrics added using the `add_metric()` API."
#~ msgstr ""

#~ msgid "A list of `Metric` objects."
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`~keras.src.optimizers.schedules.learning_rate_schedule.LearningRateSchedule`"
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`~tensornetwork.backends.abstract_backend.AbstractBackend`,"
#~ " "
#~ ":py:class:`~tensorcircuit.backends.abstract_backend.ExtendedBackend`"
#~ msgstr ""

#~ msgid "[description], defaults to be None"
#~ msgstr ""

#~ msgid ":return:the backend specific random state object :rtype: Any"
#~ msgstr ""

#~ msgid ""
#~ "If ‘left’, the index of the first"
#~ " suitable location found is given. If"
#~ " ‘right’, return the last such index."
#~ " If there is no suitable index, "
#~ "return either 0 or N (where N "
#~ "is the length of a), defaults to"
#~ " \"left\""
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`~tensornetwork.backends.jax.jax_backend.JaxBackend`, "
#~ ":py:class:`~tensorcircuit.backends.abstract_backend.ExtendedBackend`"
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`~tensornetwork.backends.numpy.numpy_backend.NumPyBackend`,"
#~ " "
#~ ":py:class:`~tensorcircuit.backends.abstract_backend.ExtendedBackend`"
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`~tensornetwork.backends.pytorch.pytorch_backend.PyTorchBackend`,"
#~ " "
#~ ":py:class:`~tensorcircuit.backends.abstract_backend.ExtendedBackend`"
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`~tensornetwork.backends.tensorflow.tensorflow_backend.TensorFlowBackend`,"
#~ " "
#~ ":py:class:`~tensorcircuit.backends.abstract_backend.ExtendedBackend`"
#~ msgstr ""

#~ msgid "Bases: :py:class:`~tensorcircuit.abstractcircuit.AbstractCircuit`"
#~ msgstr ""

#~ msgid ""
#~ "Take measurement to the given quantum"
#~ " lines. This method is jittable is"
#~ " and about 100 times faster than "
#~ "unjit version!"
#~ msgstr ""

#~ msgid ""
#~ "Sampling bistrings from the circuit "
#~ "output based on quantum amplitudes. "
#~ "Reference: arXiv:1201.3974."
#~ msgstr ""

#~ msgid "Sampled bit string and the corresponding theoretical probability."
#~ msgstr ""

#~ msgid "get the 2^n length probability vector over computational basis"
#~ msgstr ""

#~ msgid "probability vector"
#~ msgstr ""

#~ msgid ""
#~ "sample format, defaults to None as "
#~ "backward compatibility check the doc in"
#~ " :py:meth:`tensorcircuit.quantum.measurement_results`"
#~ msgstr ""

#~ msgid "Bases: :py:class:`list`"
#~ msgstr ""

#~ msgid "Quantum circuit: the state simulator"
#~ msgstr ""

#~ msgid "Bases: :py:class:`~tensorcircuit.basecircuit.BaseCircuit`"
#~ msgstr ""

#~ msgid ""
#~ "If not None, the initial state of"
#~ " the circuit is taken as ``inputs``"
#~ " instead of :math:`\\vert 0\\rangle^n` "
#~ "qubits, defaults to None."
#~ msgstr ""

#~ msgid "0 for spin up, 1 for spin down, defaults to be 0."
#~ msgstr ""

#~ msgid "Compute :math:`\\langle bra\\vert ops \\vert ket\\rangle`."
#~ msgstr ""

#~ msgid "Bases: :py:class:`BaseException`"
#~ msgstr ""

#~ msgid "Bases: :py:class:`~tensorcircuit.cloud.abstraction.TCException`"
#~ msgstr ""

#~ msgid "Bases: :py:class:`~tensorcircuit.cloud.abstraction.TaskException`"
#~ msgstr ""

#~ msgid "Bases: :py:class:`Exception`"
#~ msgstr ""

#~ msgid "Bases: :py:class:`~tensorcircuit.compiler.composed_compiler.Compiler`"
#~ msgstr ""

#~ msgid "Bases: :py:class:`~tensorcircuit.densitymatrix.DMCircuit`"
#~ msgstr ""

#~ msgid ""
#~ "h_fun should return a **SPARSE** "
#~ "Hamiltonian matrix with input arguments "
#~ "time and *args"
#~ msgstr ""

#~ msgid ""
#~ "h_fun should return a dense Hamiltonian"
#~ " matrix with input arguments time and"
#~ " *args"
#~ msgstr ""

#~ msgid ""
#~ "Fast implementation of static full "
#~ "Hamiltonian evolution (default as imaginary"
#~ " time)"
#~ msgstr ""

#~ msgid "result dynamics on ``tlist``"
#~ msgstr ""

#~ msgid "Bases: :py:class:`~tensornetwork.network_components.Node`"
#~ msgstr ""

#~ msgid "Bases: :py:class:`~tensorcircuit.gates.GateF`"
#~ msgstr ""

#~ msgid ""
#~ "R(\\theta, \\alpha, \\phi) = j \\cos(\\theta) I\n"
#~ "- j \\cos(\\phi) \\sin(\\alpha) \\sin(\\theta) X\n"
#~ "- j \\sin(\\phi) \\sin(\\alpha) \\sin(\\theta) Y\n"
#~ "- j \\sin(\\theta) \\cos(\\alpha) Z\n"
#~ "\n"
#~ msgstr ""

#~ msgid "Bases: :py:class:`~tensorcircuit.keras.QuantumLayer`"
#~ msgstr ""

#~ msgid ""
#~ "Bases: "
#~ ":py:class:`~tensornetwork.matrixproductstates.finite_mps.FiniteMPS`"
#~ msgstr ""

#~ msgid "Apply a MPO to the MPS"
#~ msgstr ""

#~ msgid "Apply a n-qubit gate by transforming the gate to MPO"
#~ msgstr ""

#~ msgid "Reduce the bond dimension between two adjacent sites by SVD"
#~ msgstr ""

#~ msgid "IMPORT"
#~ msgstr ""

#~ msgid "Bases: :py:class:`~tensorcircuit.quantum.QuOperator`"
#~ msgstr ""

#~ msgid "count_tuple format"
#~ msgstr ""

#~ msgid "convert_vector to count_dict_bin or count_dict_int"
#~ msgstr ""

#~ msgid "tensor in shape [2**n]"
#~ msgstr ""

#~ msgid "Compute the Rényi entropy of order :math:`k` by given density matrix."
#~ msgstr ""

#~ msgid "The order of Rényi entropy, default is 2."
#~ msgstr ""

#~ msgid "The :math:`k` th order of Rényi entropy."
#~ msgstr ""

#~ msgid ""
#~ "Compute the Rényi free energy of "
#~ "the corresponding density matrix and "
#~ "Hamiltonian."
#~ msgstr ""

#~ msgid ""
#~ "transform ``sample_int`` or ``sample_bin`` "
#~ "form results to other forms specified"
#~ " by ``format``"
#~ msgstr ""

#~ msgid "measurement shots results in ``sample_int`` or ``sample_bin`` format"
#~ msgstr ""

#~ msgid ""
#~ "see the doc in the doc in "
#~ ":py:meth:`tensorcircuit.quantum.measurement_results`, defaults"
#~ " to \"count_vector\""
#~ msgstr ""

#~ msgid "sample_int to count_tuple"
#~ msgstr ""

#~ msgid "int sample to bin sample"
#~ msgstr ""

#~ msgid "in shape [trials] of int elements in the range [0, 2**n)"
#~ msgstr ""

#~ msgid "in shape [trials, n] of element (0, 1)"
#~ msgstr ""

#~ msgid "tensorcircuit.templates.ensemble"
#~ msgstr ""

#~ msgid "Bases: :py:class:`~tensorcircuit.torchnn.QuantumNet`"
#~ msgstr ""

#~ msgid "Adds a child module to the current module."
#~ msgstr ""

#~ msgid ""
#~ "Applies ``fn`` recursively to every "
#~ "submodule (as returned by ``.children()``) "
#~ "as well as self. Typical use "
#~ "includes initializing the parameters of "
#~ "a model (see also :ref:`nn-init-"
#~ "doc`)."
#~ msgstr ""

#~ msgid "Returns an iterator over module buffers."
#~ msgstr ""

#~ msgid "Returns an iterator over immediate children modules."
#~ msgstr ""

#~ msgid "Moves all model parameters and buffers to the CPU."
#~ msgstr ""

#~ msgid "Moves all model parameters and buffers to the GPU."
#~ msgstr ""

#~ msgid "Sets the module in evaluation mode."
#~ msgstr ""

#~ msgid "Set the extra representation of the module"
#~ msgstr ""

#~ msgid "Defines the computation performed at every call."
#~ msgstr ""

#~ msgid ""
#~ "Returns the buffer given by ``target``"
#~ " if it exists, otherwise throws an"
#~ " error."
#~ msgstr ""

#~ msgid ""
#~ "Returns any extra state to include "
#~ "in the module's state_dict. Implement "
#~ "this and a corresponding "
#~ ":func:`set_extra_state` for your module if "
#~ "you need to store extra state. "
#~ "This function is called when building"
#~ " the module's `state_dict()`."
#~ msgstr ""

#~ msgid ""
#~ "Returns the parameter given by "
#~ "``target`` if it exists, otherwise "
#~ "throws an error."
#~ msgstr ""

#~ msgid ""
#~ "Returns the submodule given by "
#~ "``target`` if it exists, otherwise "
#~ "throws an error."
#~ msgstr ""

#~ msgid "Moves all model parameters and buffers to the IPU."
#~ msgstr ""

#~ msgid ""
#~ "Copies parameters and buffers from "
#~ ":attr:`state_dict` into this module and "
#~ "its descendants. If :attr:`strict` is "
#~ "``True``, then the keys of "
#~ ":attr:`state_dict` must exactly match the "
#~ "keys returned by this module's "
#~ ":meth:`~torch.nn.Module.state_dict` function."
#~ msgstr ""

#~ msgid "Returns an iterator over all modules in the network."
#~ msgstr ""

#~ msgid ""
#~ "Returns an iterator over module buffers,"
#~ " yielding both the name of the "
#~ "buffer as well as the buffer "
#~ "itself."
#~ msgstr ""

#~ msgid ""
#~ "Returns an iterator over immediate "
#~ "children modules, yielding both the name"
#~ " of the module as well as the"
#~ " module itself."
#~ msgstr ""

#~ msgid ""
#~ "Returns an iterator over all modules "
#~ "in the network, yielding both the "
#~ "name of the module as well as "
#~ "the module itself."
#~ msgstr ""

#~ msgid ""
#~ "Returns an iterator over module "
#~ "parameters, yielding both the name of"
#~ " the parameter as well as the "
#~ "parameter itself."
#~ msgstr ""

#~ msgid "Returns an iterator over module parameters."
#~ msgstr ""

#~ msgid "Registers a backward hook on the module."
#~ msgstr ""

#~ msgid "Adds a buffer to the module."
#~ msgstr ""

#~ msgid "Registers a forward hook on the module."
#~ msgstr ""

#~ msgid "Registers a forward pre-hook on the module."
#~ msgstr ""

#~ msgid "Registers a backward pre-hook on the module."
#~ msgstr ""

#~ msgid ""
#~ "Registers a post hook to be run"
#~ " after module's ``load_state_dict`` is "
#~ "called."
#~ msgstr ""

#~ msgid "Adds a parameter to the module."
#~ msgstr ""

#~ msgid "See :meth:`torch.Tensor.share_memory_`"
#~ msgstr ""

#~ msgid ""
#~ "Returns a dictionary containing references "
#~ "to the whole state of the module."
#~ msgstr ""

#~ msgid "Moves and/or casts the parameters and buffers."
#~ msgstr ""

#~ msgid ""
#~ "Moves the parameters and buffers to "
#~ "the specified device without copying "
#~ "storage."
#~ msgstr ""

#~ msgid "Sets the module in training mode."
#~ msgstr ""

#~ msgid "Moves all model parameters and buffers to the XPU."
#~ msgstr ""

#~ msgid ""
#~ "Sets gradients of all model parameters"
#~ " to zero. See similar function under"
#~ " :class:`torch.optim.Optimizer` for more context."
#~ msgstr ""

#~ msgid "Bases: :py:class:`~torch.nn.modules.module.Module`"
#~ msgstr ""

