Welcome and congratulations! You have found TensorCircuit. 👏
TensorCircuit is an open-source high-performance quantum computing software framework in Python.
- It is built for humans. 👽
- It is designed for speed, flexibility and elegance. 🚀
- It is empowered by advanced tensor network simulator engine. 🔋
- It is ready for quantum hardware access with CPU/GPU/QPU (local/cloud) hybrid solutions. 🖥
- It is implemented with industry-standard machine learning frameworks: TensorFlow, JAX, and PyTorch. 🤖
- It is compatible with machine learning engineering paradigms: automatic differentiation, just-in-time compilation, vectorized parallelism and GPU acceleration. 🛠
With the help of TensorCircuit, now get ready to efficiently and elegantly solve interesting and challenging quantum computing problems from academic research prototype to industry application deployment.
TensorCircuit is created and maintained by Shi-Xin Zhang and this version is released by Tencent Quantum Lab.
The current core authors of TensorCircuit are Shi-Xin Zhang and Yu-Qin Chen. We also thank contributions from the lab and the open source community.
If you have any further questions or collaboration ideas, please use the issue tracker or forum below, or send email to shixinzhang#tencent.com.
- Source code: https://github.com/tencent-quantum-lab/tensorcircuit
- Documentation: https://tensorcircuit.readthedocs.io
- Software Whitepaper (published in Quantum): https://quantum-journal.org/papers/q-2023-02-02-912/
- Issue Tracker: https://github.com/tencent-quantum-lab/tensorcircuit/issues
- Forum: https://github.com/tencent-quantum-lab/tensorcircuit/discussions
- PyPI page: https://pypi.org/project/tensorcircuit
- DockerHub page: https://hub.docker.com/repository/docker/tensorcircuit/tensorcircuit
- Research and projects based on TensorCircuit: https://github.com/tencent-quantum-lab/tensorcircuit#research-and-applications
- Tencent Quantum Cloud Service: https://quantum.tencent.com/cloud/
The following documentation sections briefly introduce TensorCircuit to the users and developpers.
.. toctree:: :maxdepth: 2 quickstart.rst advance.rst faq.rst sharpbits.rst infras.rst contribution.rst
The following documentation sections include integrated examples in the form of Jupyter Notebook.
.. toctree-filt:: :maxdepth: 2 :zh:tutorial.rst :zh:whitepapertoc.rst :en:tutorial_cn.rst :en:whitepapertoc_cn.rst :en:textbooktoc.rst
.. toctree:: :maxdepth: 2 modules.rst