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https://github.com/tensorcircuit/tensorcircuit-ng

Tensor network based quantum software framework: next generation
https://github.com/tensorcircuit/tensorcircuit-ng

automatic-differentiation gpu jax machine-learning neural-network nisq open-quantum-systems pytorch quantum-algorithm quantum-circuit quantum-computing quantum-dynamics quantum-hardware quantum-machine-learning quantum-noise quantum-simulation tensor-network tensorflow

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Tensor network based quantum software framework: next generation

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README 

        


  

    

  






  

  

    

  

  

  

    

  

  

  

    

  

  

  

    

  




 English |  įŽ€äŊ“中文 



TensorCircuit-NG is an open-source high-performance quantum software framework, supporting for automatic differentiation, just-in-time compiling, hardware acceleration, and vectorized parallelism, providing unified infrastructures and interfaces for quantum programming. It can compose quantum circuits, neural networks and tensor networks seamlessly with high simulation efficiency and flexibility.



TensorCircuit-NG is built on top of modern machine learning frameworks: Jax, TensorFlow, and PyTorch. It is specifically suitable for large-scale simulations of quantum-classical hybrid paradigm and variational quantum algorithms in ideal, noisy, Clifford, approximate and analog cases. It also supports quantum hardware access and provides CPU/GPU/QPU hybrid deployment solutions.



TensorCircuit-NG is [fully compatible](https://tensorcircuit-ng.readthedocs.io/en/latest/faq.html#what-is-the-relation-between-tensorcircuit-and-tensorcircuit-ng) with TensorCircuit with more new features and bug fixes (support latest `numpy>2` and `qiskit>1`).



## Getting Started



Please begin with [Quick Start](/docs/source/quickstart.rst) in the [full documentation](https://tensorcircuit-ng.readthedocs.io/).



For more information on software usage, sota algorithm implementation and engineer paradigm demonstration, please refer to 80+ [example scripts](/examples) and 30+ [tutorial notebooks](https://tensorcircuit-ng.readthedocs.io/en/latest/#tutorials). API docstrings and test cases in [tests](/tests) are also informative.



For beginners, please refer to [quantum computing lectures with TC-NG](https://github.com/sxzgroup/qc_lecture) to learn both quantum computing basis and representative usage of TensorCircuit-NG.



The following are some minimal demos.



- Circuit construction:



```python

import tensorcircuit as tc

c = tc.Circuit(2)

c.H(0)

c.CNOT(0,1)

c.rx(1, theta=0.2)

print(c.wavefunction())

print(c.expectation_ps(z=[0, 1]))

print(c.sample(allow_state=True, batch=1024, format="count_dict_bin"))

```



- Runtime behavior customization:



```python

tc.set_backend("tensorflow")

tc.set_dtype("complex128")

tc.set_contractor("greedy")

```



- Automatic differentiation with jit:



```python

def forward(theta):

    c = tc.Circuit(2)

    c.R(0, theta=theta, alpha=0.5, phi=0.8)

    return tc.backend.real(c.expectation((tc.gates.z(), [0])))



g = tc.backend.grad(forward)

g = tc.backend.jit(g)

theta = tc.array_to_tensor(1.0)

print(g(theta))

```



   More highlight features for TensorCircuit (click for details) 



- Sparse Hamiltonian generation and expectation evaluation:



```python

n = 6

pauli_structures = []

weights = []

for i in range(n):

    pauli_structures.append(tc.quantum.xyz2ps({"z": [i, (i + 1) % n]}, n=n))

    weights.append(1.0)

for i in range(n):

    pauli_structures.append(tc.quantum.xyz2ps({"x": [i]}, n=n))

    weights.append(-1.0)

h = tc.quantum.PauliStringSum2COO(pauli_structures, weights)

print(h)

# BCOO(complex64[64, 64], nse=448)

c = tc.Circuit(n)

c.h(range(n))

energy = tc.templates.measurements.operator_expectation(c, h)

# -6

```



- Large-scale simulation with tensor network engine



```python

# tc.set_contractor("cotengra-30-10")

n=500

c = tc.Circuit(n)

c.h(0)

c.cx(range(n-1), range(1, n))

c.expectation_ps(z=[0, n-1], reuse=False)

```



- Density matrix simulator and quantum info quantities



```python

c = tc.DMCircuit(2)

c.h(0)

c.cx(0, 1)

c.depolarizing(1, px=0.1, py=0.1, pz=0.1)

dm = c.state()

print(tc.quantum.entropy(dm))

print(tc.quantum.entanglement_entropy(dm, [0]))

print(tc.quantum.entanglement_negativity(dm, [0]))

print(tc.quantum.log_negativity(dm, [0]))

```



## Install



The package is written in pure Python and can be obtained via pip as:



```python

pip install tensorcircuit-ng

```



We recommend you install this package with tensorflow also installed as:



```python

pip install "tensorcircuit-ng[tensorflow]"

```



Other optional dependencies include `[torch]`, `[jax]`, `[qiskit]` and `[cloud]`.



Try nightly build for the newest features:



```python

pip install tensorcircuit-nightly

```



We also have [Docker support](/docker).



## Advantages



- Tensor network simulation engine based



- JIT, AD, vectorized parallelism compatible



- GPU support, quantum device access support, hybrid deployment support



- HPC native, distributed simulation enabled, multiple devices/hosts support



- Efficiency



  - Time: 10 to 10^6+ times acceleration compared to TensorFlow Quantum, Pennylane or Qiskit



  - Space: 600+ qubits 1D VQE workflow (converged energy inaccuracy: < 1%)



- Elegance



  - Flexibility: customized contraction, multiple ML backend/interface choices, multiple dtype precisions, multiple QPU providers



  - API design: quantum for humans, less code, more power



- Batteries included



  

   Tons of amazing features and built in tools for research (click for details) 



  - Support **super large circuit simulation** using tensor network engine.



  - Support **noisy simulation** with both Monte Carlo and density matrix (tensor network powered) modes.



  - Support **stabilizer circuit simulation** with stim backend



  - Support **approximate simulation** with MPS-TEBD modes.



  - Support **analog/digital hybrid simulation** (time dependent Hamiltonian evolution, **pulse** level simulation) with neural ode modes.



  - Support **Fermion Gaussian state** simulation with expectation, entanglement, measurement, ground state, real and imaginary time evolution.



  - Support **qudits simulation**.



  - Support **parallel** quantum circuit evaluation across **multiple GPUs**.



  - Highly customizable **noise model** with gate error and scalable readout error.



  - Support for **non-unitary** gate and post-selection simulation.



  - Support **real quantum devices access** from different providers.



  - **Scalable readout error mitigation** native to both bitstring and expectation level with automatic qubit mapping consideration.



  - **Advanced quantum error mitigation methods** and pipelines such as ZNE, DD, RC, etc.



  - Support **MPS/MPO** as representations for input states, quantum gates and observables to be measured.



  - Support **vectorized parallelism** on circuit inputs, circuit parameters, circuit structures, circuit measurements and these vectorization can be nested.



  - Gradients can be obtained with both **automatic differenation** and parameter shift (vmap accelerated) modes.



  - **Machine learning interface/layer/model** abstraction in both TensorFlow, PyTorch and Jax for both numerical simulation and real QPU experiments.



  - Circuit sampling supports both final state sampling and perfect sampling from tensor networks.



  - Light cone reduction support for local expectation calculation.



  - Highly customizable tensor network contraction path finder with opteinsum and cotengra interface.



  - Observables are supported in measurement, sparse matrix, dense matrix and MPO format.



  - Super fast weighted sum Pauli string Hamiltonian matrix generation.



  - Reusable common circuit/measurement/problem templates and patterns.



  - Jittable classical shadow infrastructures.



  - SOTA quantum algorithm and model implementations.



  - Support hybrid workflows and pipelines with CPU/GPU/QPU hardware from local/cloud/hpc resources using tf/torch/jax/cupy/numpy frameworks all at the same time.



  



## Contributing



### Status



This project is created and maintained by [Shi-Xin Zhang](https://github.com/refraction-ray) with current core authors [Shi-Xin Zhang](https://github.com/refraction-ray) and [Yu-Qin Chen](https://github.com/yutuer21) (see the [brief history](/HISTORY.md) of TensorCircuit and TensorCircuit-NG). We also thank [contributions](https://github.com/tensorcircuit/tensorcircuit-ng/graphs/contributors) from the open source community.



### Citation



If this project helps in your research, please cite our software whitepaper to acknowledge the work put into the development of TensorCircuit-NG.



[TensorCircuit: a Quantum Software Framework for the NISQ Era](https://quantum-journal.org/papers/q-2023-02-02-912/) (published in Quantum)



which is also a good introduction to the software.



Research works citing TensorCircuit can be highlighted in [Research and Applications section](https://github.com/tensorcircuit/tensorcircuit-ng#research-and-applications).



### Guidelines



For contribution guidelines and notes, see [CONTRIBUTING](/CONTRIBUTING.md).



We welcome [issues](https://github.com/tensorcircuit/tensorcircuit-ng/issues), [PRs](https://github.com/tensorcircuit/tensorcircuit-ng/pulls), and [discussions](https://github.com/tensorcircuit/tensorcircuit-ng/discussions) from everyone, and these are all hosted on GitHub.



### License



TensorCircuit-NG is open source, released under the Apache License, Version 2.0.



### Contributors



  

    

      Shixin Zhang
Shixin Zhang
đŸ’ģ 📖 💡 🤔 🚇 🚧 đŸ”Ŧ 👀 🌍 ⚠ī¸ ✅ đŸ“ĸ đŸ’Ŧ đŸ’ĩ

      Yuqin Chen
Yuqin Chen
đŸ’ģ 📖 💡 🤔 đŸ”Ŧ ⚠ī¸ ✅ đŸ“ĸ

      Jiezhong Qiu
Jiezhong Qiu
đŸ’ģ 💡 🤔 đŸ”Ŧ

      Weitang Li
Weitang Li
đŸ’ģ 📖 🤔 đŸ”Ŧ ⚠ī¸ đŸ“ĸ

      Jiace Sun
Jiace Sun
đŸ’ģ 📖 💡 🤔 đŸ”Ŧ ⚠ī¸

      Zhouquan Wan
Zhouquan Wan
đŸ’ģ 📖 💡 🤔 đŸ”Ŧ ⚠ī¸ ✅

    

    

      Shuo Liu
Shuo Liu
💡 đŸ”Ŧ ✅

      Hao Yu
Hao Yu
đŸ’ģ 📖 🚇 ⚠ī¸ ✅

      Xinghan Yang
Xinghan Yang
📖 🌍 ✅

      JachyMeow
JachyMeow
✅ 🌍

      Zhaofeng Ye
Zhaofeng Ye
🎨

      erertertet
erertertet
đŸ’ģ 📖 ⚠ī¸

    

    

      Yicong Zheng
Yicong Zheng
✅

      Zixuan Song
Zixuan Song
📖 🌍 đŸ’ģ ⚠ī¸

      Hao Xie
Hao Xie
📖

      Pramit Singh
Pramit Singh
⚠ī¸

      Jonathan Allcock
Jonathan Allcock
📖 🤔 đŸ“ĸ

      nealchen2003
nealchen2003
📖

    

    

      隐å…Ŧ观éąŧ
隐å…Ŧ观éąŧ
đŸ’ģ ⚠ī¸

      WiuYuan
WiuYuan
💡

      Felix Xu
Felix Xu
✅ đŸ’ģ ⚠ī¸

      Hong-Ye Hu
Hong-Ye Hu
📖

      peilin
peilin
✅ đŸ’ģ ⚠ī¸ 📖

      Cristian Emiliano Godinez Ramirez
Cristian Emiliano Godinez Ramirez
đŸ’ģ ⚠ī¸

    

    

      ztzhu
ztzhu
đŸ’ģ

      Rabqubit
Rabqubit
💡

      Kazuki Tsuoka
Kazuki Tsuoka
đŸ’ģ ⚠ī¸ 📖 💡

      Gopal Ramesh Dahale
Gopal Ramesh Dahale
💡

      Chanandellar Bong
Chanandellar Bong
💡

      Abhinav Deshpande
Abhinav Deshpande
đŸ’ģ

    

  



## Research and Applications



### DQAS



For the application of Differentiable Quantum Architecture Search, see [applications](/tensorcircuit/applications).



Reference paper: https://arxiv.org/abs/2010.08561 (published in QST).



### VQNHE



For the application of Variational Quantum-Neural Hybrid Eigensolver, see [applications](/tensorcircuit/applications).



Reference paper: https://arxiv.org/abs/2106.05105 (published in PRL) and https://arxiv.org/abs/2112.10380 (published in AQT).



### VQEX-MBL



For the application of VQEX on MBL phase identification, see the [tutorial](/docs/source/tutorials/vqex_mbl.ipynb).



Reference paper: https://arxiv.org/abs/2111.13719 (published in PRB).



### Stark-DTC



For the numerical demosntration of discrete time crystal enabled by Stark many-body localization, see the Floquet simulation [demo](/examples/timeevolution_trotter.py).



Reference paper: https://arxiv.org/abs/2208.02866 (published in PRL).



### RA-Training



For the numerical simulation of variational quantum algorithm training using random gate activation strategy by us, see the [project repo](https://github.com/ls-iastu/RAtraining).



Reference paper: https://arxiv.org/abs/2303.08154 (published in PRR as a Letter).



### TenCirChem



[TenCirChem](https://github.com/tencent-quantum-lab/TenCirChem) is an efficient and versatile quantum computation package for molecular properties. TenCirChem is based on TensorCircuit and is optimized for chemistry applications. The latest version TenCirChem-NG is open source and available at [TenCirChem-NG](https://github.com/tensorcircuit/TenCirChem-NG).



Reference paper: https://arxiv.org/abs/2303.10825 (published in JCTC).



### EMQAOA-DARBO



For the numerical simulation and hardware experiments with error mitigation on QAOA, see the [project repo](https://github.com/sherrylixuecheng/EMQAOA-DARBO).



Reference paper: https://arxiv.org/abs/2303.14877 (published in Communications Physics).



### NN-VQA



For the setup and simulation code of neural network encoded variational quantum eigensolver, see the [demo](/docs/source/tutorials/nnvqe.ipynb).



Reference paper: https://arxiv.org/abs/2308.01068 (published in PRApplied).



### Effective temperature in approximate ansatzes



For the simulation implementation of quantum states based on neural networks, tensor networs and quantum circuits using TensorCircuit-NG, see the [project repo](https://github.com/sxzgroup/et).



Reference paper: https://arxiv.org/abs/2411.18921.



### More works



   More research works and code projects using TensorCircuit and TensorCircuit-NG (click for details) 



- Neural Predictor based Quantum Architecture Search: https://arxiv.org/abs/2103.06524 (published in Machine Learning: Science and Technology).



- Quantum imaginary-time control for accelerating the ground-state preparation: https://arxiv.org/abs/2112.11782 (published in PRR).



- Efficient Quantum Simulation of Electron-Phonon Systems by Variational Basis State Encoder: https://arxiv.org/abs/2301.01442 (published in PRR).



- Variational Quantum Simulations of Finite-Temperature Dynamical Properties via Thermofield Dynamics: https://arxiv.org/abs/2206.05571.



- Understanding quantum machine learning also requires rethinking generalization: https://arxiv.org/abs/2306.13461 (published in Nature Communications).



- Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation: https://arxiv.org/abs/2306.11297. Code: https://github.com/s222416822/BQFL.



- Non-IID quantum federated learning with one-shot communication complexity: https://arxiv.org/abs/2209.00768 (published in Quantum Machine Intelligence). Code: https://github.com/JasonZHM/quantum-fed-infer.



- Quantum generative adversarial imitation learning: https://doi.org/10.1088/1367-2630/acc605 (published in New Journal of Physics).



- GSQAS: Graph Self-supervised Quantum Architecture Search: https://arxiv.org/abs/2303.12381 (published in Physica A: Statistical Mechanics and its Applications).



- Practical advantage of quantum machine learning in ghost imaging: https://www.nature.com/articles/s42005-023-01290-1 (published in Communications Physics).



- Zero and Finite Temperature Quantum Simulations Powered by Quantum Magic: https://arxiv.org/abs/2308.11616 (published in Quantum).



- Comparison of Quantum Simulators for Variational Quantum Search: A Benchmark Study: https://arxiv.org/abs/2309.05924.



- Statistical analysis of quantum state learning process in quantum neural networks: https://arxiv.org/abs/2309.14980 (published in NeurIPS).



- Generative quantum machine learning via denoising diffusion probabilistic models: https://arxiv.org/abs/2310.05866 (published in PRL).



- Exploring the topological sector optimization on quantum computers: https://arxiv.org/abs/2310.04291 (published in PRApplied).



- Google Summer of Code 2023 Projects (QML4HEP): https://github.com/ML4SCI/QMLHEP, https://github.com/Gopal-Dahale/qgnn-hep, https://github.com/salcc/QuantumTransformers.



- Universal imaginary-time critical dynamics on a quantum computer: https://arxiv.org/abs/2308.05408 (published in PRB).



- Absence of barren plateaus in finite local-depth circuits with long-range entanglement: https://arxiv.org/abs/2311.01393 (published in PRL).



- Non-Markovianity benefits quantum dynamics simulation: https://arxiv.org/abs/2311.17622.



- Variational post-selection for ground states and thermal states simulation: https://arxiv.org/abs/2402.07605 (published in PRB).



- Subsystem information capacity in random circuits and Hamiltonian dynamics: https://arxiv.org/abs/2405.05076.



- Symmetry restoration and quantum Mpemba effect in symmetric random circuits: https://arxiv.org/abs/2403.08459 (published in PRL).



- Quantum Mpemba effects in many-body localization systems: https://arxiv.org/abs/2408.07750.



- Supersymmetry dynamics on Rydberg atom arrays: https://arxiv.org/abs/2410.21386.



- Dynamic parameterized quantum circuits: expressive and barren-plateau free: https://arxiv.org/abs/2411.05760.



- Holographic deep thermalization: https://arxiv.org/abs/2411.03587.



- Quantum deep generative prior with programmable quantum circuits: https://www.nature.com/articles/s42005-024-01765-9 (published in Communications Physics).



If you want to highlight your research work or projects here, feel free to add by opening PR.



## Users



Our users, developers, and partners: