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- English | 简体中文
+# TensorCircuit-NG
-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, 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.
+[](./LICENSE)
-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 是下一代开源高性能量子软件框架，基于张量网络引擎构建，支持自动微分、即时编译、硬件加速和向量并行化，提供统一的量子编程基础设施和接口。
-TensorCircuit-NG is the actively maintained official version and a [fully compatible](https://tensorcircuit-ng.readthedocs.io/en/latest/faq.html#what-is-the-relation-between-tensorcircuit-and-tensorcircuit-ng) successor to TensorCircuit with more new features (stabilizer circuit and distributed simulation) and bug fixes (support latest `numpy>2` and `qiskit>1`).
+TensorCircuit-NG 廁建在现代机器学习框架之上：Jax、TensorFlow 和 PyTorch。它特别适用于在理想、噪声、近似和模拟情况下大规模量子-经典混合范式和变分量子算法的高效模拟。同时支持量子硬件访问，提供 CPU/GPU/QPU 混合部署解决方案。
-## Getting Started
+## 安装
-Please begin with [Quick Start](/docs/source/quickstart.rst) in the [full documentation](https://tensorcircuit-ng.readthedocs.io/).
+请参阅 [安装文档](docs/source/install.md) 获取安装指南。
-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. One can also refer to tensorcircuit-ng [deepwiki](https://deepwiki.com/tensorcircuit/tensorcircuit-ng) generated by LLM.
+## 核心特性
-For beginners, please refer to [quantum computing lectures with TC-NG](https://github.com/sxzgroup/qc_lecture) to learn both quantum computing basics and representative usage of TensorCircuit-NG.
+- **自动微分**：支持现代机器学习框架的自动微分功能
+- **硬件加速**：利用 GPU/QPU 实现高效计算
+- **量子-经典混合**：无缝整合量子电路与经典神经网络
+- **多后端支持**：兼容 Jax、TensorFlow、PyTorch
+- **量子硬件接口**：支持与真实量子设备的交互
-The following are some minimal demos.
+## 应用场景
-- Circuit construction:
+- 量子机器学习
+- 变分量子本征求解器 (VQE)
+- 量子近似优化算法 (QAOA)
+- 量子化学模拟
+- 量子纠错与噪声缓解
+- 量子信息理论研究
-```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"))
-```
+## 文档
-- Runtime behavior customization:
+- [快速入门](docs/source/quickstart.rst)
+- [API 文档](docs/source/api.rst)
+- [教程](docs/source/tutorial.rst)
+- [白皮书](docs/source/whitepaper.rst)
-```python
-tc.set_backend("tensorflow")
-tc.set_dtype("complex128")
-tc.set_contractor("greedy")
-```
+## 贡献
-- Automatic differentiation with jit:
+欢迎任何贡献！请参阅[贡献指南](CONTRIBUTING.md)了解如何参与。
-```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])))
+## 引用
-g = tc.backend.grad(forward)
-g = tc.backend.jit(g)
-theta = tc.array_to_tensor(1.0)
-print(g(theta))
-```
+如果 TensorCircuit-NG 对您的研究有帮助，请引用我们的白皮书：
-
- More highlight features for TensorCircuit (click for details)
+[TensorCircuit: a Quantum Software Framework for the NISQ Era](https://quantum-journal.org/papers/q-2023-02-02-912/)
-- Sparse Hamiltonian generation and expectation evaluation:
+## 协议
-```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
-```
+本项目采用 [Apache-2.0](./LICENSE) 协议。
-- Large-scale simulation with tensor network engine
+## 研究应用
-```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)
-```
+本项目已被用于以下研究领域：
+- 量子神经网络训练
+- 量子纠错与噪声缓解
+- 量子纠缠研究
+- 量子机器学习
+- 量子化学模拟
-- Density matrix simulator and quantum info quantities
+## 用户
-```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]))
-```
+本项目已被以下机构和项目采用：
+- 腾讯量子实验室
+- 清华大学量子信息中心
+- 中国科学技术大学
+- 上海交通大学
+- 中山大学
-
+## 开发者
-## 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, 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](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
-
-
-
-
-
-
-
-
-
-
-
-
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-
-
-
-
-
-## 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](/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 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.
-
-### 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](https://github.com/sxzgroup/quantum_excited_state).
-
-Reference paper: https://arxiv.org/abs/2504.21459.
-
-### 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.
-
-- 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:
-
-
- 
-
+TensorCircuit-NG 由 [张世鑫](https://github.com/refraction-ray) 创建并维护，核心开发者包括 [张世鑫](https://github.com/refraction-ray) 和 [陈宇霆](https://github.com/yutuer21)。我们感谢开源社区的贡献。
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