Executable Research Hub

Quantum Research, Made Executable

From published quantum knowledge to executable research infrastructure. This curated hub packages published methods as runnable, metadata-rich, and independently inspectable TensorCircuit-NG artifacts. Read the paper, inspect the implementation, and run the experiment yourself.

Please read: these are independent reimplementations by the TensorCircuit-NG project, not author-endorsed replications. Most are deliberately scaled down — fewer qubits, smaller bond dimensions, shorter training — so that they finish quickly on a single machine, and some make explicit modeling simplifications. Each entry documents what was changed. Treat them as executable illustrations of the physics, not as verification of the original results.

Topic
TC feature
13 reproductions
Figure 2(b) · Qutrit time-crystal response

Figure 2(b) · Qutrit time-crystal response

A Qutrit Time Crystal Stabilized with Native Chiral Interactions

A driven chiral clock model of 9 qutrits shows period-doubled magnetization and a sharp subharmonic peak in the Fourier spectrum.

2026Noah Goss et al.jax
quditfloquet-dynamicsmany-body-physics
Noise estimation convergence

Noise estimation convergence

Differentiable Maximum Likelihood Noise Estimation for Quantum Error Correction

Surface-code noise parameters are recovered by differentiating through a CopyNode tensor-network contraction and optimizing with Adam.

2026Hanyan Cao et al.jax
quantum-error-correctiontensor-network
Figure 2 · Entanglement barrier in 3-SAT

Figure 2 · Entanglement barrier in 3-SAT

Entanglement Barriers from Computational Complexity: Matrix-Product-State Approach to Satisfiability

Imaginary-time propagation of hard 3-SAT instances shows the MPS entanglement entropy peaking near the satisfiability threshold.

2026Tim Pokart et al.jax
matrix-product-stateentanglementcombinatorial-optimizationtensor-network
Figure 4(a) · Machine size versus accuracy

Figure 4(a) · Machine size versus accuracy

Exponential Quantum Advantage in Processing Massive Classical Data

Quantum oracle sketching is compared with classical streaming and sparse baselines on an MNIST 3-vs-8 task at matched accuracy.

2026Haimeng Zhao et al.jax
quantum-advantagequantum-machine-learningclassification
Figure 1(a) · Thermal energy versus temperature

Figure 1(a) · Thermal energy versus temperature

Quantum Finite Temperature Lanczos Method

Quantum Hutchinson states and a generalized eigenvalue problem give thermal energies of a 10-site TFIM without full diagonalization.

2026Gian Gentinetta et al.jax
quantum-algorithmfinite-temperaturemany-body-physics
Figure 2(b) · Catastrophic forgetting

Figure 2(b) · Catastrophic forgetting

Quantum Continual Learning Overcoming Catastrophic Forgetting

An 8-qubit amplitude-encoded classifier trained sequentially on MNIST and permuted MNIST forgets the first task as it learns the second.

2021Wenjie Jiang et al.jax
quantum-machine-learningclassificationvariational-algorithm
SEBD accuracy versus bond dimension

SEBD accuracy versus bond dimension

Efficient Classical Simulation of Random Shallow 2D Quantum Circuits

Spatial Evolution Block Decimation contracts a random shallow 2D circuit as a PEPS-to-MPS sweep, checked against exact amplitudes.

2020John Napp et al.numpy
classical-simulationtensor-networkmatrix-product-staterandom-circuit
Figure 6 · Single-qubit universal classifier

Figure 6 · Single-qubit universal classifier

Data Re-uploading for a Universal Quantum Classifier

One qubit re-uploads the input at every layer to act as a universal classifier, trained with L-BFGS on the JAX backend.

2019Adrián Pérez-Salinas et al.jax
quantum-machine-learningclassificationvariational-algorithm
Figure 6 · QCBM learning a Gaussian mixture

Figure 6 · QCBM learning a Gaussian mixture

Differentiable Learning of Quantum Circuit Born Machine

A 10-qubit hardware-efficient Born machine trained with an MMD loss to fit a Gaussian mixture, differentiated end to end with JAX.

2018Jin-Guo Liu et al.jax
quantum-machine-learninggenerative-modelvariational-algorithm
Figure 13(a) · Measurement-induced entanglement

Figure 13(a) · Measurement-induced entanglement

Measurement-Induced Phase Transitions in the Dynamics of Entanglement

Small monitored random circuits show volume-law to area-law entanglement across the measurement-induced transition.

2018Brian Skinner et al.jax
entanglementmany-body-physicsrandom-circuit
Figure 2(c) · QCNN phase recognition

Figure 2(c) · QCNN phase recognition

Quantum Convolutional Neural Networks

quimb DMRG ground states feed a TensorCircuit QCNN that separates an SPT phase from the trivial one at N = 11 and N = 45 sites.

2018Iris Cong et al.jax
quantum-machine-learningclassificationtopological-phasetensor-network
Section IV · QAOA on a ring of disagrees

Section IV · QAOA on a ring of disagrees

A Quantum Approximate Optimization Algorithm

TensorCircuit QAOA improves the expected MaxCut ratio on an even ring toward the analytic (2p+1)/(2p+2) curve.

2014Edward Farhi et al.jax
combinatorial-optimizationquantum-algorithmvariational-algorithm
Figure 6 · Topological quantum-walk edge state

Figure 6 · Topological quantum-walk edge state

Topological Phenomena in Quantum Walks

A split-step quantum walk retains a localized interface probability only when the two sides have different winding numbers.

2011Takuya Kitagawajax
topological-phasefloquet-dynamicsquantum-algorithm