AI Skill Hub 强烈推荐：TensorCircuit-NG 是一款优质的Agent工作流。AI 综合评分 8.5 分，在同类工具中表现稳健。如果你正在寻找可靠的Agent工作流解决方案，这是一个值得深入了解的选择。
TensorCircuit-NG 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
TensorCircuit-NG 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
# 方式一：pip 安装（推荐）
pip install tensorcircuit-ng
# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install tensorcircuit-ng
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/tensorcircuit/tensorcircuit-ng
cd tensorcircuit-ng
pip install -e .
# 验证安装
python -c "import tensorcircuit_ng; print('安装成功')"
# 命令行使用
tensorcircuit-ng --help
# 基本用法
tensorcircuit-ng input_file -o output_file
# Python 代码中调用
import tensorcircuit_ng
# 示例
result = tensorcircuit_ng.process("input")
print(result)
# tensorcircuit-ng 配置文件示例（config.yml） app: name: "tensorcircuit-ng" debug: false log_level: "INFO" # 运行时指定配置文件 tensorcircuit-ng --config config.yml # 或通过环境变量配置 export TENSORCIRCUIT_NG_API_KEY="your-key" export TENSORCIRCUIT_NG_OUTPUT_DIR="./output"
<p align="center"> <a href="https://github.com/tensorcircuit/tensorcircuit-ng"> <img width=90% src="docs/source/statics/logong.png"> </a> </p>
<p align="center"> <a href="https://github.com/tensorcircuit/tensorcircuit-ng/actions/workflows/ci.yml"> <img src="https://img.shields.io/github/actions/workflow/status/tensorcircuit/tensorcircuit-ng/ci.yml?branch=master" /> </a> <a href="https://tensorcircuit-ng.readthedocs.io/"> <img src="https://img.shields.io/badge/docs-link-green.svg?logo=read-the-docs"/> </a> <a href="https://arxiv.org/abs/2205.10091"> <img src="https://img.shields.io/badge/arXiv-2205.10091-teal.svg"/> </a> <a href="https://arxiv.org/abs/2602.14167"> <img src="https://img.shields.io/badge/arXiv-2602.14167-teal.svg"/> </a> <a href="https://pypi.org/project/tensorcircuit-ng/"> <img src="https://img.shields.io/pypi/v/tensorcircuit-ng.svg?logo=pypi"/> </a> <a href="./LICENSE"> <img src="https://img.shields.io/badge/license-Apache%202.0-blue.svg?logo=apache"/> </a> </p>
<p align="center"> English | <a href="README_cn.md"> 简体中文 </a></p>
TensorCircuit-NG is the next-generation open-source high-performance quantum software framework, and the world's first AI-native quantum programming platform purpose-built for agentic research and automated scientific discovery.
TensorCircuit-NG is 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 (Circuit), noisy (DMCircuit), Clifford (StabilizerCircuit), qudit (QuditCircuit), approximate (MPSCircuit), analog (AnalogCircuit), symmetric (U1Circuit) and fermionic (FGSCircuit) cases. It also supports quantum hardware access and provides CPU/GPU/QPU hybrid deployment solutions.
TensorCircuit-NG is the technical successor to TensorCircuit, led and maintained by the original TensorCircuit development team. This distribution has served as the primary home for the framework's evolution, addressing critical maintenance gaps (numpy > 2.0, qiskit > 1.0) and feature enhancements. As a fully compatible drop-in replacement, TensorCircuit-NG delivers next-gen capabilities—including stabilizer/qudit/analog/symmetric circuit simulation and multi-node multi-GPU distributed simulation.
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 170+ example scripts and 40+ 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, Google Code Wiki, 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 package is written in pure Python and can be obtained via pip as:
pip install tensorcircuit-ngWe recommend you install this package with tensorflow or jax also installed as:
pip install "tensorcircuit-ng[tensorflow]"Other optional dependencies include [torch], [jax], [qiskit] and [cloud].
Try nightly build for the newest features:
pip install tensorcircuit-nightlyFor contribution guidelines and notes, see CONTRIBUTING.
We welcome issues, PRs, and discussions from everyone, and these are all hosted on GitHub.
The following are some minimal demos.
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"))tc.set_backend("jax")
tc.set_dtype("complex128")
tc.set_contractor("cotengra")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))<details> <summary> More highlight features for TensorCircuit (click for details) </summary>
```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)
For the application of Differentiable Quantum Architecture Search, see applications.
Reference paper: https://arxiv.org/abs/2010.08561 (published in QST).
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).
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).
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总体来看，TensorCircuit-NG 是一款质量优秀的Agent工作流，在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态，建议收藏备用，结合自身场景选择合适时机引入使用。
| 原始名称 | tensorcircuit-ng |
| Topics | aiquantumjaxgpudistributed-training |
| GitHub | https://github.com/tensorcircuit/tensorcircuit-ng |
| License | Apache-2.0 |
| 语言 | Python |
收录时间：2026-06-07 · 更新时间：2026-06-07 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。
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