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# Copyright (C) 2020, The TensorCircuit Authors
# This file is distributed under the same license as the tensorcircuit
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msgstr ""
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"PO-Revision-Date: 2026-03-31 13:10+0800\n"
"Last-Translator: Gemini CLI\n"
"Language: zh_CN\n"
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#: ../../source/infras.rst:3
msgid "TensorCircuit-NG: What is inside?"
msgstr "TensorCircuit-NG：内部原理"

#: ../../source/infras.rst:5
msgid ""
"This part of the documentation is mainly for advanced users and "
"developers who want to learn more about what happened behind the scene "
"and delve into the codebase."
msgstr "这部分文档主要面向希望了解幕后原理并深入研究代码库的高级用户和开发人员。"

#: ../../source/infras.rst:9
msgid "Overview of Modules"
msgstr "模块概述"

#: ../../source/infras.rst:11
msgid "**Core Modules:**"
msgstr "**核心模块：**"

#: ../../source/infras.rst:13
msgid ""
":py:mod:`tensorcircuit.abstractcircuit` and "
":py:mod:`tensorcircuit.basecircuit`: Hierarchical abstraction of circuit "
"class."
msgstr ":py:mod:`tensorcircuit.abstractcircuit` 和 :py:mod:`tensorcircuit.basecircuit`: 线路类的分层抽象。"

#: ../../source/infras.rst:15
msgid ""
":py:mod:`tensorcircuit.circuit`: The core object "
":py:obj:`tensorcircuit.circuit.Circuit`. It supports circuit "
"construction, simulation, representation, and visualization without noise"
" or with noise using the Monte Carlo trajectory approach."
msgstr ":py:mod:`tensorcircuit.circuit`: 核心对象 :py:obj:`tensorcircuit.circuit.Circuit` 。它支持无噪声或使用蒙特卡洛轨迹方法处理有噪声的线路构建、仿真、表示和可视化。"

#: ../../source/infras.rst:17
msgid ""
":py:mod:`tensorcircuit.cons`: Runtime ML backend, dtype and contractor "
"setups. We provide three sets of set methods for global setup, function "
"level setup using function decorators, and context setup using ``with`` "
"context managers. We also include customized contractor infrastructures "
"in this module."
msgstr ":py:mod:`tensorcircuit.cons`: 运行时的机器学习后端、数据类型和 contractor 设置。我们提供了三组设置方法：全局设置、使用函数装饰器的函数级设置，以及使用 ``with`` 上下文管理器的上下文级设置。我们还在此模块中包含了定制的 contractor 基础设施。"

#: ../../source/infras.rst:19
msgid ""
":py:mod:`tensorcircuit.gates`: Definition of quantum gates, either fixed "
"ones or parameterized ones, as well as "
":py:obj:`tensorcircuit.gates.GateF` class for gates."
msgstr ":py:mod:`tensorcircuit.gates`: 固定或参数化量子门的定义，以及用于门的 :py:obj:`tensorcircuit.gates.GateF` 类。"

#: ../../source/infras.rst:21
msgid "**Backend Agnostic Abstraction:**"
msgstr "**后端无关抽象：**"

#: ../../source/infras.rst:23
msgid ""
":py:mod:`tensorcircuit.backends` provides a set of backend API and the "
"corresponding implementation on Numpy, Jax, TensorFlow, and PyTorch "
"backends. These backends are inherited from the TensorNetwork package and"
" are highly customized."
msgstr ":py:mod:`tensorcircuit.backends` 提供了一套后端 API 及其在 NumPy、JAX、TensorFlow 和 PyTorch 后端上的对应实现。这些后端继承自 TensorNetwork 包并经过了高度定制。"

#: ../../source/infras.rst:25
msgid "**Noisy Simulation Related Modules:**"
msgstr "**噪声模拟相关模块：**"

#: ../../source/infras.rst:27
msgid ":py:mod:`tensorcircuit.channels`: Definition of quantum noise channels."
msgstr ":py:mod:`tensorcircuit.channels`: 量子噪声通道的定义。"

#: ../../source/infras.rst:29
msgid ""
":py:mod:`tensorcircuit.densitymatrix`: Referenced and highly efficient "
"implementation of ``tc.DMCircuit`` class, with similar set API of "
"``tc.Circuit`` while simulating the noise in the full form of the density"
" matrix."
msgstr ":py:mod:`tensorcircuit.densitymatrix`: ``tc.DMCircuit`` 类的高效参考实现，具有与 ``tc.Circuit`` 类似的 API，同时通过密度矩阵的完整形式模拟噪声。"

#: ../../source/infras.rst:31
msgid ""
":py:mod:`tensorcircuit.noisemodel`: The global noise configuration and "
"circuit noisy method APIs"
msgstr ":py:mod:`tensorcircuit.noisemodel`: 全局噪声配置和线路噪声方法 API。"

#: ../../source/infras.rst:33
msgid "**ML Interfaces Related Modules:**"
msgstr "**机器学习接口相关模块：**"

#: ../../source/infras.rst:35
msgid ""
":py:mod:`tensorcircuit.interfaces`: Provide interfaces when quantum "
"simulation backend is different from neural libraries. Currently include "
"PyTorch, TensorFlow, NumPy and SciPy optimizer interfaces."
msgstr ":py:mod:`tensorcircuit.interfaces`: 当量子模拟后端与神经库不同时提供接口。目前包括 PyTorch、TensorFlow、NumPy 和 SciPy 优化器接口。"

#: ../../source/infras.rst:37
msgid ""
":py:mod:`tensorcircuit.keras`: Provide TensorFlow Keras layers, as well "
"as wrappers of jitted function, save/load from tf side."
msgstr ":py:mod:`tensorcircuit.keras`: 提供 TensorFlow Keras 层，以及即时编译 (JIT) 函数的包装器，支持在 TensorFlow 端进行保存/加载。"

#: ../../source/infras.rst:39
msgid ":py:mod:`tensorcircuit.torchnn`: Provide PyTorch nn Modules."
msgstr ":py:mod:`tensorcircuit.torchnn`: 提供 PyTorch 神经网络模块。"

#: ../../source/infras.rst:41
msgid "**MPS and MPO Utiliy Modules:**"
msgstr "**MPS 和 MPO 实用模块：**"

#: ../../source/infras.rst:43
msgid ""
":py:mod:`tensorcircuit.quantum`: Provide definition and classes for "
"Matrix Product States as well as Matrix Product Operators, we also "
"include various quantum physics and quantum information quantities in "
"this module."
msgstr ":py:mod:`tensorcircuit.quantum`: 提供矩阵乘积态 (MPS) 和矩阵乘积算符 (MPO) 的定义和类，我们还在此模块中包含了各种量子物理和量子信息相关的量。"

#: ../../source/infras.rst:45
msgid "**MPS Based Simulator Modules:**"
msgstr "**基于 MPS 的模拟器模块：**"

#: ../../source/infras.rst:47
msgid ""
":py:mod:`tensorcircuit.mps_base`: Customized and jit/AD compatible MPS "
"class from TensorNetwork package."
msgstr ":py:mod:`tensorcircuit.mps_base`: 来自 TensorNetwork 包的定制化、兼容 JIT/AD 的 MPS 类。"

#: ../../source/infras.rst:49
msgid ""
":py:mod:`tensorcircuit.mpscircuit`: "
":py:obj:`tensorcircuit.mpscircuit.MPSCircuit` class with similar (but "
"subtly different) APIs as ``tc.Circuit``, where the simulation engine is "
"based on MPS TEBD."
msgstr ":py:mod:`tensorcircuit.mpscircuit`: :py:obj:`tensorcircuit.mpscircuit.MPSCircuit` 类，具有与 ``tc.Circuit`` 类似（但有细微差别）的 API，仿真引擎基于 MPS TEBD。"

#: ../../source/infras.rst:51
msgid "**Specialized Simulator Modules:**"
msgstr "**专门模拟器模块：**"

#: ../../source/infras.rst:53
msgid ""
":py:mod:`tensorcircuit.analogcircuit`: Hybrid digital-analog quantum "
"simulation with time-dependent Hamiltonians."
msgstr ":py:mod:`tensorcircuit.analogcircuit`: 具有随时间变化的哈密顿量的数模混合量子模拟。"

#: ../../source/infras.rst:55
msgid ""
":py:mod:`tensorcircuit.fgs`: Fermion Gaussian State (FGS) simulator for "
"non-interacting fermionic systems."
msgstr ":py:mod:`tensorcircuit.fgs`: 用于非相互作用费米子系统的费米子高斯态 (FGS) 模拟器。"

#: ../../source/infras.rst:57
msgid ""
":py:mod:`tensorcircuit.pauliprop`: Pauli Propagation Engine (PPE) for "
"tracking Heisenberg-picture evolution of Pauli observables."
msgstr ":py:mod:`tensorcircuit.pauliprop`: 用于跟踪泡利可观测核海森堡图像演化的泡利传播引擎 (PPE)。"

#: ../../source/infras.rst:59
msgid ""
":py:mod:`tensorcircuit.quditcircuit`: Higher-dimensional qudit state-"
"vector simulator supporting $d$-level systems ($3 \\le d \\le 36$)."
msgstr ":py:mod:`tensorcircuit.quditcircuit`: 支持 $d$ 能级系统（$3 \\le d \\le 36$）的高维 Qudit 状态向量模拟器。"

#: ../../source/infras.rst:61
msgid ""
":py:mod:`tensorcircuit.stabilizercircuit`: Clifford/stabilizer circuit "
"simulator using the high-performance Stim backend."
msgstr ":py:mod:`tensorcircuit.stabilizercircuit`: 使用高性能 Stim 后端的 Clifford/稳定器线路模拟器。"

#: ../../source/infras.rst:63
msgid ""
":py:mod:`tensorcircuit.u1circuit`: $U(1)$ symmetry-protected circuit "
"simulator for particle number conserving systems."
msgstr ":py:mod:`tensorcircuit.u1circuit`: 用于粒子数守恒系统的 $U(1)$ 对称性保护线路模拟器。"

#: ../../source/infras.rst:65
msgid "**Advanced Simulation Utilities:**"
msgstr "**高级模拟实用程序：**"

#: ../../source/infras.rst:67
msgid ""
":py:mod:`tensorcircuit.shadows`: Utilities for classical shadows, "
"efficient state estimation, and entropy calculation."
msgstr ":py:mod:`tensorcircuit.shadows`: 用于经典影子 (classical shadows)、高效状态估计和熵计算的工具。"

#: ../../source/infras.rst:69
msgid ""
":py:mod:`tensorcircuit.timeevol`: Advanced time evolution engines "
"including Krylov, Lanczos, ODE-based, and Chebyshev methods."
msgstr ":py:mod:`tensorcircuit.timeevol`: 高级时间演化引擎，包括 Krylov、Lanczos、基于 ODE 和切比雪夫方法。"

#: ../../source/infras.rst:71
msgid "**Supplemental Modules:**"
msgstr "**辅助模块：**"

#: ../../source/infras.rst:73
msgid ""
":py:mod:`tensorcircuit.simplify`: Provide tools and utility functions to "
"simplify the tensornetworks before the real contractions."
msgstr ":py:mod:`tensorcircuit.simplify`: 提供在真正收缩之前简化张量网络的工具和实用函数。"

#: ../../source/infras.rst:75
msgid ""
":py:mod:`tensorcircuit.experimental`: Experimental functions, long and "
"stable support is not guaranteed."
msgstr ":py:mod:`tensorcircuit.experimental`: 实验性功能，不保证长期稳定的支持。"

#: ../../source/infras.rst:77
msgid ""
":py:mod:`tensorcircuit.utils`: Some general function tools that are not "
"quantum at all."
msgstr ":py:mod:`tensorcircuit.utils`: 一些与量子完全无关的通用函数工具。"

#: ../../source/infras.rst:79
msgid ":py:mod:`tensorcircuit.vis`: Visualization code for circuit drawing."
msgstr ":py:mod:`tensorcircuit.vis`: 用于线路绘制的可视化代码。"

#: ../../source/infras.rst:81
msgid ""
":py:mod:`tensorcircuit.translation`: Translate circuit object to circuit "
"object in other quantum packages."
msgstr ":py:mod:`tensorcircuit.translation`: 将线路对象翻译为其他量子软件包中的线路对象。"

#: ../../source/infras.rst:83
msgid "**Processing and error mitigation on sample results:**"
msgstr "**采样结果的处理和误差缓解：**"

#: ../../source/infras.rst:85
msgid ""
":py:mod:`tensorcircuit.results`: Provide tools to process count dict and "
"to apply error mitigation."
msgstr ":py:mod:`tensorcircuit.results`: 提供处理计数字典并应用误差缓解的工具。"

#: ../../source/infras.rst:87
msgid "**Cloud quantum hardware access module:**"
msgstr "**云端量子硬件访问模块：**"

#: ../../source/infras.rst:89
msgid ""
":py:mod:`tensorcircuit.cloud`: Provide quantum cloud SDK that can access "
"and program the real quantum hardware."
msgstr ":py:mod:`tensorcircuit.cloud`: 提供量子云 SDK，可以访问并对真实量子硬件进行编程。"

#: ../../source/infras.rst:91
msgid ""
":py:mod:`tensorcircuit.compiler`: Provide compiler chains to compile and "
"transform quantum circuits."
msgstr ":py:mod:`tensorcircuit.compiler`: 提供编译器链以编译和转换量子线路。"

#: ../../source/infras.rst:93
msgid "**Shortcuts and Templates for Circuit Manipulation:**"
msgstr "**线路操作的快捷方式和模板：**"

#: ../../source/infras.rst:95
msgid ""
":py:mod:`tensorcircuit.templates`: provide handy shortcuts functions for "
"expectation or circuit building patterns."
msgstr ":py:mod:`tensorcircuit.templates`: 为期望计算或线路构建模式提供便捷的快捷函数。"

#: ../../source/infras.rst:97
msgid "**Applications:**"
msgstr "**应用：**"

#: ../../source/infras.rst:99
msgid ""
":py:mod:`tensorcircuit.applications`: most code here is not maintained "
"and deprecated, use at your own risk."
msgstr ":py:mod:`tensorcircuit.applications`: 此处的大多数代码未维护且已弃用，使用风险自负。"

#: ../../source/infras.rst:103
msgid ""
"Recommend reading order -- only read the part of code you care about for "
"your purpose. If you want to get an overview of the codebase, please read"
" ``tc.circuit`` followed by ``tc.cons`` and ``tc.gates``."
msgstr "推荐阅读顺序 —— 仅阅读您感兴趣的代码部分。如果您想对代码库有一个概览，请阅读 ``tc.circuit`` ，然后是 ``tc.cons`` 和 ``tc.gates`` 。"

#: ../../source/infras.rst:108
msgid "Relation between TensorCircuit-NG and TensorNetwork-NG"
msgstr "TensorCircuit-NG 与 TensorNetwork-NG 的关系"

#: ../../source/infras.rst:110
msgid ""
"TensorCircuit has a strong connection with the `TensorNetwork package "
"<https://github.com/google/TensorNetwork>`_ released by Google. Since the"
" TensorNetwork package has poor documentation and tutorials, most of the "
"time, we need to delve into the codebase of TensorNetwork to figure out "
"what happened. In other words, to read the TensorCircuit codebase, one "
"may have to frequently refer to the TensorNetwork codebase. As "
"TensorNetwork package is not maintained anymore, we rely on the "
"TensorNetwork-NG package maintained by the same authors of TensorCircuit-"
"NG."
msgstr "TensorCircuit 与谷歌发布的 `TensorNetwork 包 <https://github.com/google/TensorNetwork>`_ 有着紧密的联系。由于 TensorNetwork 包的文档和教程较少，大多数情况下，我们需要深入研究 TensorNetwork 的代码库来理清原理。换句话说，要阅读 TensorCircuit 的代码库，可能需要经常参考 TensorNetwork 的代码库。由于 TensorNetwork 包已不再维护，我们现在依赖于由 TensorCircuit-NG 相同作者维护的 TensorNetwork-NG 包。"

#: ../../source/infras.rst:112
msgid ""
"Inside TensorCircuit-NG, we heavily utilize TensorNetwork-related APIs "
"from the TensorNetwork package and highly customized several modules from"
" TensorNetwork by inheritance and rewriting:"
msgstr "在 TensorCircuit-NG 内部，我们大量使用了 TensorNetwork 包中与张量网络相关的 API，并通过继承和重写高度定制了 TensorNetwork 的几个模块："

#: ../../source/infras.rst:114
msgid ""
"We implement our own /backends from TensorNetwork's /backends by adding "
"much more APIs and fixing lots of bugs in TensorNetwork's implementations"
" on certain backends via monkey patching. (The upstream is inactive and "
"not that responsive anyhow.)"
msgstr "我们通过增加更多 API 以及使用猴子补丁修复 TensorNetwork 在某些后端实现中的许多错误，从 TensorNetwork 的 /backends 实现了我们自己的 /backends。（毕竟上游已不再活跃且响应缓慢。）"

#: ../../source/infras.rst:116
msgid ""
"We borrow TensorNetwork's code in /quantum to our ``tc.quantum`` module, "
"since TensorNetwork has no ``__init__.py`` file to export these MPO and "
"MPS related objects. Of course, we have made substantial improvements "
"since then."
msgstr "我们将 TensorNetwork 在 /quantum 中的代码借用到我们的 ``tc.quantum`` 模块中，因为 TensorNetwork 没有 ``__init__.py`` 文件来导出这些 MPO 和 MPS 相关对象。当然，自那时起我们已经做出了实质性的改进。"

#: ../../source/infras.rst:118
msgid ""
"We borrow the TensorNetwork's code in /matrixproductstates as "
"``tc.mps_base`` for bug fixing and jit/AD compatibility, so that we have "
"better support for our MPS based quantum circuit simulator."
msgstr "我们借用了 /matrixproductstates 中 TensorNetwork 的代码作为 ``tc.mps_base`` ，用于错误修复和 JIT/AD 兼容性，以便更好地支持我们基于 MPS 的量子线路模拟器。"

#: ../../source/infras.rst:122
msgid "Relations of Circuit-like classes"
msgstr "类线路 (Circuit-like) 类的关系"

#: ../../source/infras.rst:135
msgid ""
"``AnalogCircuit`` and ``QuditCircuit`` are high-level wrappers for hybrid"
" digital-analog and qudit systems respectively, but they share similar "
"APIs with the circuit classes above."
msgstr "``AnalogCircuit`` 和 ``QuditCircuit`` 分别是针对数模混合系统和 Qudit 系统的高层包装器，但它们与上述线路类共享类似的 API。"

#: ../../source/infras.rst:140
msgid "QuOperator/QuVector and MPO/MPS"
msgstr "QuOperator/QuVector 和 MPO/MPS"

#: ../../source/infras.rst:142
msgid ""
":py:class:`tensorcircuit.quantum.QuOperator`, "
":py:class:`tensorcircuit.quantum.QuVector` and "
":py:class:`tensorcircuit.quantum.QuAdjointVector` are classes adopted "
"from TensorNetwork package. They behave like a matrix/vector (column or "
"row) when interacting with other ingredients while the inner structure is"
" maintained by the tensornetwork for efficiency and compactness."
msgstr ":py:class:`tensorcircuit.quantum.QuOperator` 、:py:class:`tensorcircuit.quantum.QuVector` 和 :py:class:`tensorcircuit.quantum.QuAdjointVector` 是从 TensorNetwork 包中采用的类。它们在与其他组件交互时表现得像矩阵/向量（列或行），而内部结构由张量网络维护，以确保效率和紧凑性。"

#: ../../source/infras.rst:145
msgid ""
"We use code examples and associated tensor diagrams to illustrate these "
"object abstractions."
msgstr "我们使用代码示例和相关的张量图来说明这些对象抽象。"

#: ../../source/infras.rst:149
msgid ""
"``QuOperator`` can express MPOs and ``QuVector`` can express MPSs, but "
"they can express more than these fixed structured tensor networks."
msgstr "``QuOperator`` 可以表示 MPO， ``QuVector`` 可以表示 MPS，但它们可以表示的内容不仅限于这些固定结构的张量网络。"

#: ../../source/infras.rst:177
msgid ""
"Note how in this example, ``matrix`` is not a typical MPO but still can "
"be expressed as ``QuOperator``. Indeed, any tensor network with two sets "
"of dangling edges of the same dimension can be treated as ``QuOperator``."
" ``QuVector`` is even more flexible since we can treat all dangling edges"
" as the vector dimension."
msgstr "请注意在此示例中， ``matrix`` 并不是一个典型的 MPO，但仍然可以表示为 ``QuOperator`` 。实际上，任何具有两组相同维度悬空边的张量网络都可以被视为 ``QuOperator`` 。 ``QuVector`` 甚至更加灵活，因为我们可以将所有悬空边都视为向量维度。"

#: ../../source/infras.rst:179
msgid ""
"Also, note how ``^`` is overloaded as ``tn.connect`` to connect edges "
"between different nodes in TensorNetwork. And indexing the node gives the"
" edges of the node, eg. ``n1[0]`` means the first edge of node ``n1``."
msgstr "另外，请注意 ``^`` 如何被重载为 ``tn.connect`` ，以连接 TensorNetwork 中不同节点之间的边。对节点进行索引会得到该节点的边，例如 ``n1[0]`` 表示节点 ``n1`` 的第一条边。"

#: ../../source/infras.rst:181
msgid ""
"The convention to define the ``QuOperator`` is firstly giving "
"``out_edges`` (left index or row index of the matrix) and then giving "
"``in_edges`` (right index or column index of the matrix). The edges list "
"contains edge objects from the TensorNetwork library."
msgstr "定义 ``QuOperator`` 的惯例是首先给出 ``out_edges`` （矩阵的左索引或行索引），然后给出 ``in_edges`` （矩阵的右索引或列索引）。边列表包含来自 TensorNetwork 库的边对象。"

#: ../../source/infras.rst:183
msgid ""
"Such QuOperator/QuVector abstraction support various calculations only "
"possible on matrix/vectors, such as matmul (``@``), adjoint "
"(``.adjoint()``), scalar multiplication (``*``), tensor product (``|``), "
"and partial trace (``.partial_trace(subsystems_to_trace_out)``). To "
"extract the matrix information of these objects, we can use ``.eval()`` "
"or ``.eval_matrix()``, the former keeps the shape information of the "
"tensor network while the latter gives the matrix representation with "
"shape rank 2."
msgstr "此类 QuOperator/QuVector 抽象支持各种仅在矩阵/向量上可能的计算，例如矩阵乘法 (``@``)、伴随 (``.adjoint()``)、标量乘法 (``*``)、张量积 (``|``) 和偏迹 (``.partial_trace(subsystems_to_trace_out)``)。要提取这些对象的矩阵信息，可以使用 ``.eval()`` 或 ``.eval_matrix()`` ，前者保留张量网络的形状信息，而后者提供秩为 2 的矩阵表示。"

#: ../../source/infras.rst:188
msgid "Quantum Cloud SDK: Layerwise API design"
msgstr "量子云 SDK：分层 API 设计"

#: ../../source/infras.rst:190
msgid "From lower level to higher level, a view of API layers invoking QPU calls"
msgstr "从底层到高层，调用 QPU 的 API 层级视图"

#: ../../source/infras.rst:192
msgid ""
"Vendor specific implementation of functional API in, e.g., "
":py:mod:`tensorcircuit.cloud.tencent`"
msgstr "供应商特定的函数式 API 实现，例如 :py:mod:`tensorcircuit.cloud.tencent` 。"

#: ../../source/infras.rst:194
msgid ""
"Provider agnostic functional lower level API for task/device management "
"in :py:mod:`tensorcircuit.cloud.apis`"
msgstr "与供应商无关的底层任务/设备管理函数式 API，见 :py:mod:`tensorcircuit.cloud.apis` 。"

#: ../../source/infras.rst:196
msgid ""
"Object oriented abstraction for Provider/Device/Task in "
":py:mod:`tensorcircuit.cloud.abstraction`"
msgstr "面向提供商/设备/任务的对象抽象，见 :py:mod:`tensorcircuit.cloud.abstraction` 。"

#: ../../source/infras.rst:198
msgid ""
"Unified batch submission interface as standarized in "
":py:meth:`tensorcircuit.cloud.wrapper.batch_submit_template`"
msgstr "标准化的统一批量提交接口，见 :py:meth:`tensorcircuit.cloud.wrapper.batch_submit_template` 。"

#: ../../source/infras.rst:200
msgid ""
"Numerical and experimental unified all-in-one interface as "
":py:meth:`tensorcircuit.cloud.wrapper.batch_expectation_ps`"
msgstr "数值模拟与实验统一的全功能接口，见 :py:meth:`tensorcircuit.cloud.wrapper.batch_expectation_ps` 。"

#: ../../source/infras.rst:202
msgid ""
"Application level code with QPU calls built directly on "
"``batch_expectation_ps`` or more fancy algorithms can be built on "
"``batch_submit_func`` so that these algorithms can be reused as long as "
"one function ``batch_submit_func`` is defined for a given vendor (cheaper"
" than defining a new provider from lower level)."
msgstr "直接构建在 ``batch_expectation_ps`` 上的具有 QPU 调用的应用级代码，或者可以构建在 ``batch_submit_func`` 上的更复杂的算法，这样只要为特定供应商定义了一个 ``batch_submit_func`` 函数，这些算法就可以被复用（这比从底层定义一个新的提供商成本更低）。"

#: ../../source/infras.rst:207
msgid ""
"For compiler, error mitigation and results post-processing parts, they "
"can be carefully designed to decouple with the QPU calls, so they are "
"separately implemented in :py:mod:`tensorcircuit.compiler` and "
":py:mod:`tensorcircuit.results`, and they can be independently useful "
"even without tc's cloud access."
msgstr "对于编译器、误差缓解和结果后处理部分，它们经过精心设计以与 QPU 调用解耦，因此在 :py:mod:`tensorcircuit.compiler` 和 :py:mod:`tensorcircuit.results` 中独立实现，即使没有 TensorCircuit 的云端访问，它们也可以独立发挥作用。"
