tensorcircuit-ng
The next-gen tensor network based quantum software framework: superseding the original TensorCircuit
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Repository
The next-gen tensor network based quantum software framework: superseding the original TensorCircuit
Basic Info
- Host: GitHub
- Owner: tensorcircuit
- License: apache-2.0
- Language: Python
- Default Branch: master
- Homepage: https://tensorcircuit-ng.readthedocs.io/
- Size: 15.6 MB
Statistics
- Stars: 42
- Watchers: 1
- Forks: 11
- Open Issues: 6
- Releases: 6
Topics
Metadata Files
README.md
English | 简体中文
TensorCircuit-NG is the next-generation open-source high-performance quantum software framework, built upon tensornetwork engines, supporting for automatic differentiation, just-in-time compiling, hardware acceleration, vectorized parallelism and distributed training, 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, analog and fermionic cases. It also supports quantum hardware access and provides CPU/GPU/QPU hybrid deployment solutions.
TensorCircuit-NG is the actively maintained official version and a fully compatible successor to TensorCircuit with more new features (stabilizer circuit, multi-card distributed simulation, etc.) and bug fixes (support latest numpy>2 and qiskit>1).
Getting Started
Please begin with Quick Start in the full documentation.
For more information on software usage, sota algorithm implementation and engineer paradigm demonstration, please refer to 90+ example scripts and 30+ tutorial notebooks. API docstrings and test cases in tests are also informative. One can also refer to AI-native docs for tensorcircuit-ng: Devin Deepwiki and Context7 MCP.
For beginners, please refer to quantum computing lectures with TC-NG to learn both quantum computing basics 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.arraytotensor(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 or jax 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.
Advantages
Tensor network simulation engine based
JIT, AD, vectorized parallelism compatible
GPU support, QPU 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 with current core authors Shi-Xin Zhang and Yu-Qin Chen (see the brief history of TensorCircuit and TensorCircuit-NG). We also thank contributions 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 (published in Quantum)
which is also a good introduction to the software.
Research works citing TensorCircuit can be highlighted in Research and Applications section.
Guidelines
For contribution guidelines and notes, see CONTRIBUTING.
We welcome issues, PRs, and 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
Research and Applications
TensorCircuit-NG is a powerful framework for driving research and applications in quantum computing. Below are examples of published academic works and open-source projects that utilize TensorCircuit-NG.
DQAS
For the application of Differentiable Quantum Architecture Search, see applications.
Reference paper: https://arxiv.org/abs/2010.08561 (published in QST).
VQNHE
For the application of Variational Quantum-Neural Hybrid Eigensolver, see 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.
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.
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.
Reference paper: https://arxiv.org/abs/2303.08154 (published in PRR as a Letter).
TenCirChem
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.
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.
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.
Reference paper: https://arxiv.org/abs/2308.01068 (published in PRApplied).
Effective temperature in ansatzes
For the simulation implementation of quantum states based on neural networks, tensor networs and quantum circuits using TensorCircuit-NG, see the project repo.
Reference paper: https://arxiv.org/abs/2411.18921.
A Unified Variational Framework for Quantum Excited States
For the simulation code and data for variational optimization of simutaneous excited states, see the project repo.
Reference paper: https://arxiv.org/abs/2504.21459.
Quantum Machine Unlearning
For the simulation code for the work "superior resilience to poisoning and amenability to unlearning in quantum machine learning", see the project repo.
Reference paper: https://arxiv.org/abs/2508.02422.
Low Weight Pauli Propagation Simulation
For the simulation code and data for the work on low weight Pauli propagation in the context of variational quantum algorithms, see the project repo.
Reference paper: https://arxiv.org/abs/2508.06358.
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 QST). - Subsystem information capacity in random circuits and Hamiltonian dynamics: https://arxiv.org/abs/2405.05076 (published in Quantum). Code implementation: https://github.com/sxzgroup/subsystem_information_capacity. - 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 (published in PRB). - Dynamic parameterized quantum circuits: expressive and barren-plateau free: https://arxiv.org/abs/2411.05760. - Holographic deep thermalization: https://arxiv.org/abs/2411.03587 (published in Nature Communications). - Quantum deep generative prior with programmable quantum circuits: https://www.nature.com/articles/s42005-024-01765-9 (published in Communications Physics). - Symmetry Breaking Dynamics in Quantum Many-Body Systems: https://arxiv.org/abs/2501.13459. - Entanglement growth and information capacity in a quasiperiodic system with a single-particle mobility edge: https://arxiv.org/abs/2506.18076. - Hilbert subspace imprint: a new mechanism for non-thermalization: https://arxiv.org/abs/2506.11922. - A Neural-Guided Variational Quantum Algorithm for Efficient Sign Structure Learning in Hybrid Architectures: https://arxiv.org/abs/2507.07555.If you want to highlight your research work or projects here, feel free to add by opening PR.
Users
Our users, developers, and partners:
Owner
- Name: TensorCircuit
- Login: tensorcircuit
- Kind: organization
- Repositories: 1
- Profile: https://github.com/tensorcircuit
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you find this software helpful in your research, please cite it as below."
authors:
- family-names: "Zhang"
given-names: "Shi-Xin"
- family-names: "Chen"
given-names: "Yu-Qin"
title: "TensorCircuit-NG"
version: 1.0.0
date-released: 2020-04-19
url: "https://github.com/tensorcircuit/tensorcircuit-ng"
preferred-citation:
type: article
authors:
- family-names: "Zhang"
given-names: "Shi-Xin"
- family-names: "Allcock"
given-names: "Jonathan"
- family-names: "Wan"
given-names: "Zhou-Quan"
- family-names: "Liu"
given-names: "Shuo"
- family-names: "Sun"
given-names: "Jiace"
- family-names: "Yu"
given-names: "Hao"
- family-names: "Yang"
given-names: "Xing-Han"
- family-names: "Qiu"
given-names: "Jiezhong"
- family-names: "Ye"
given-names: "Zhaofeng"
- family-names: "Chen"
given-names: "Yu-Qin"
- family-names: "Lee"
given-names: "Chee-Kong"
- family-names: "Zheng"
given-names: "Yi-Cong"
- family-names: "Jian"
given-names: "Shao-Kai"
- family-names: "Yao"
given-names: "Hong"
- family-names: "Hsieh"
given-names: "Chang-Yu"
- family-names: "Zhang"
given-names: "Shengyu"
doi: "10.22331/q-2023-02-02-912"
journal: "Quantum"
month: 2
pages: 912
title: "TensorCircuit: a Quantum Software Framework for the NISQ Era"
volume: 7
year: 2023
GitHub Events
Total
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Last Year
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- Issues event: 12
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- Fork event: 12
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Last synced: over 1 year ago
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| Name | Commits | |
|---|---|---|
| refraction-ray | k****l@f****m | 1,030 |
| Xinghan_Yang | y****n@o****m | 78 |
| allcontributors[bot] | 4****] | 74 |
| YHPeter | y****3@h****m | 55 |
| yutuer21 | 5****9@q****m | 53 |
| PeilinZHENG | p****g@g****m | 48 |
| JiaceSun | j****3@c****u | 47 |
| Xinghan Yang | 2****4@q****m | 33 |
| Xinghan Yang | 6****s | 32 |
| MarkSong535 | 7****5 | 21 |
| Kazuki Tsuoka | k****a@g****p | 19 |
| xptree | j****u@o****m | 17 |
| Felix Xu | 6****5 | 14 |
| weitang li | w****i@t****m | 13 |
| liwt31 | l****1@1****m | 13 |
| Zhou-quan Wan | 9****9@q****m | 13 |
| Felix Xu | x****9@i****k | 11 |
| MarkSong535 | m****g@m****h | 10 |
| eurethia | 3****1@q****m | 9 |
| ls-iastu | l****0@m****n | 9 |
| JachyMeow | 1****w | 7 |
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| Erertertet | e****t@g****m | 5 |
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| JAllcock | j****k@g****m | 3 |
| Hong-Ye Hu | 5****u | 2 |
| EmilianoG-byte | e****8@g****m | 2 |
| Gopal Dahale | d****7@g****m | 2 |
| Hao Xie | x****8@m****n | 2 |
| and 7 more... | ||
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Last synced: 6 months ago
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Past Year
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- Bot issues: 0
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pypi.org: tensorcircuit-ng
High performance unified quantum computing framework for the NISQ era
- Homepage: https://github.com/tensorcircuit/tensorcircuit-ng
- Documentation: https://tensorcircuit-ng.readthedocs.io/
- License: apache-2.0
-
Latest release: 1.3.0
published 7 months ago