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‎README.md‎

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Please begin with [Quick Start](/docs/source/quickstart.rst) in the [full documentation](https://tensorcircuit-ng.readthedocs.io/).
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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) or [Context7 MCP](https://context7.com/tensorcircuit/tensorcircuit-ng).
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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 AI-native docs for tensorcircuit-ng: [Devin Deepwiki](https://deepwiki.com/tensorcircuit/tensorcircuit-ng) and [Context7 MCP](https://context7.com/tensorcircuit/tensorcircuit-ng).
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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.
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‎README_cn.md‎

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<p align="center"> <a href="README.md">English</a> | 简体中文 </p>
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TensorCircuit-NG 是下一代量子软件框架，完美支持自动微分、即时编译、硬件加速和向量并行化。
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TensorCircuit-NG 是下一代量子软件框架，完美支持自动微分、即时编译、硬件加速、向量并行化和分布式训练。
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TensorCircuit-NG 建立在现代机器学习框架 Jax, TensorFlow, PyTorch 之上，支持机器学习后端无关的统一界面。 其特别适用于理想情况、含噪声情况、稳定子情况及可控近似情况下，大规模量子经典混合范式和变分量子算法的高效模拟。
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TensorCircuit-NG 建立在现代机器学习框架 Jax, TensorFlow, PyTorch 之上，支持机器学习后端无关的统一界面。 其特别适用于理想情况、含噪声情况、稳定子情况、可控近似情况、连续动力学情况及费米子情况下，大规模量子经典混合范式和变分量子算法的高效模拟。其可以高效地编织和模拟量子线路、张量网络和神经网络组成的混合计算图。
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TensorCircuit-NG 现在支持真实量子硬件连接和实验，并提供优雅的 CPU/GPU/QPU 混合部署训练方案（v0.9+）。
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TensorCircuit-NG 现在支持真实量子硬件连接和实验，并提供优雅的 CPU/GPU/QPU 硬件混合部署训练方案。
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TensorCircuit-NG 是目前积极维护的唯一官方版本，是 TensorCircuit 的[完全兼容](https://github.com/orgs/tensorcircuit/discussions/19)的升级版本，它包含了更多新功能（例如稳定子线路、多卡分布式模拟等）和错误修复（例如支持最新的 numpy>2 和 qiskit>1）。
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## 入门
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请从 [完整文档](https://tensorcircuit-ng.readthedocs.io/) 中的 [快速上手](/docs/source/quickstart.rst) 开始。
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有关软件用法，算法实现和工程范式演示的更多信息和介绍，请参阅 80+ [示例脚本](/examples) 和 30+ [案例教程](https://tensorcircuit-ng.readthedocs.io/en/latest/#tutorials)。 [测试](/tests) 用例和 API docstring 也提供了丰富的使用信息。
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TensorCircuit-NG 也支持 AI 原生编程资源：[Devin Deepwiki](https://deepwiki.com/tensorcircuit/tensorcircuit-ng) 和 [Context7 MCP](https://context7.com/tensorcircuit/tensorcircuit-ng).
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初学者也可以参考[量子计算教程](https://github.com/sxzgroup/qc_lecture)学习量子计算基础和 TensorCircuit-NG 的典型用法.
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以下是一些最简易的演示。

‎llm.md‎

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# TensorCircuit-NG Repository Guide for AI Agents
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## Repository Overview
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TensorCircuit is a high-performance unified quantum computing framework designed for the NISQ (Noisy Intermediate-Scale Quantum) era. It provides a comprehensive set of tools for quantum circuit simulation with support for multiple backends including Numpy, TensorFlow, JAX, and PyTorch.
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## Documentation and AI Native Services
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### Official Documentation
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- Main Documentation: https://tensorcircuit-ng.readthedocs.io/
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- Quick Start Guide: https://tensorcircuit-ng.readthedocs.io/en/latest/quickstart.html
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- Tutorials: https://tensorcircuit-ng.readthedocs.io/en/latest/tutorial.html
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- API Reference: Available in docstrings throughout the codebase
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### AI-Native Documentation Services
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- Devin Deepwiki: https://deepwiki.com/tensorcircuit/tensorcircuit-ng
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- Context7 MCP: https://context7.com/tensorcircuit/tensorcircuit-ng
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### Educational Resources
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- Quantum Computing Lectures with TC-NG: https://github.com/sxzgroup/qc_lecture
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## Configuration and Dependencies
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### Core Dependencies
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- numpy
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- scipy
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- tensorflow
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- tensornetwork-ng
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- graphviz
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- jax
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- jaxlib
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- networkx
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- optax
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### Development Dependencies
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- mypy (1.11.2)
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- pytest (7.4.4)
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- black (with jupyter support)
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- pylint (3.2.6)
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- sphinx (>=4.0)
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### Configuration Files
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1. `requirements/` - Contains various requirement files:
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- [requirements.txt](file:///Users/shixin/Nutstore%20Files/newwork/quantum-information/codebases/tensorcircuit/requirements/requirements.txt) - Core dependencies
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- [requirements-dev.txt](file:///Users/shixin/Nutstore%20Files/newwork/quantum-information/codebases/tensorcircuit/requirements/requirements-dev.txt) - Development tools
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- [requirements-extra.txt](file:///Users/shixin/Nutstore%20Files/newwork/quantum-information/codebases/tensorcircuit/requirements/requirements-extra.txt) - Optional dependencies
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- [requirements-types.txt](file:///Users/shixin/Nutstore%20Files/newwork/quantum-information/codebases/tensorcircuit/requirements/requirements-types.txt) - Type checking dependencies
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2. [mypy.ini](file:///Users/shixin/Nutstore%20Files/newwork/quantum-information/codebases/tensorcircuit/mypy.ini) - Type checking configuration with strict mode enabled
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3. `.pylintrc` - Code style enforcement with specific rules enabled
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4. [pytest.ini](file:///Users/shixin/Nutstore%20Files/newwork/quantum-information/codebases/tensorcircuit/pytest.ini) - Test configuration with deprecation warnings filtered
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## Common Bash Commands
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### Development Checks
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```bash
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# Run all checks (black, mypy, pylint, pytest, sphinx)
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bash check_all.sh
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# Equivalent to the following individual checks:
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black . --check # Code formatting check
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mypy tensorcircuit # Type checking
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pylint tensorcircuit tests examples/*.py # Code linting
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pytest -n auto --cov=tensorcircuit -vv -W ignore::DeprecationWarning # Run tests
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# Run all tests with coverage report
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pytest --cov=tensorcircuit --cov-report=xml -svv --benchmark-skip
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# Run specific test file
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pytest tests/test_circuit.py
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# Install dependencies
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pip install --upgrade pip
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pip install -r requirements/requirements.txt
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pip install -r requirements/requirements-dev.txt
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pip install -r requirements/requirements-extra.txt
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pip install -r requirements/requirements-types.txt
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```
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## AI Agent Best Practices
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### Efficient Code Navigation
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- Use the search function to find specific classes and functions rather than browsing files
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- Look for example usage in the /examples/ directory when learning new features
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- Check tests in /tests/ directory for detailed usage examples
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- Refer to docstrings for API documentation
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### Common Patterns in the Codebase
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- Backend-agnostic operations through the tc.backend interface
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- JIT compilation support via tc.backend.jit(), JIT is prefered for performance
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- Automatic differentiation support via tc.backend.grad()
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- Vectorized operations using tc.backend.vmap patterns
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- Context managers for temporary configuration changes
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### Working with Quantum Concepts
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- Familiarity with quantum computing basics (qubits, gates, measurements)
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- Understanding of tensor network concepts for advanced features
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- Knowledge of different quantum computing paradigms (digital, analog, noisy, etc.)
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## Additional Information
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### Test Structure
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- Tests located in /tests/ directory
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- Example scripts in /examples/ directory also serve as integration tests
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- CI runs example demos to ensure functionality
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### Documentation
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- Documentation built with Sphinx
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- Both English and Chinese versions generated
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- Located in /docs/ directory
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### Package Distribution
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- Distributed as tensorcircuit-ng package
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- Supports extra dependencies for specific backends (tensorflow, jax, torch, qiskit, cloud)
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### Core Design Principles
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- Unified interface across multiple backends
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- High performance through tensor network optimizations
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- Extensible architecture for quantum computing research
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- Compatibility with major quantum computing frameworks
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### Branch Strategy
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- main/master branch for stable releases
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- beta branch for nightly builds (as seen in nightly_release.yml)
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- pull requests for feature development

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