index.rst

TensorCircuit Documentation

Welcome and congratulations! You have found TensorCircuit. 👏

Introduction

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.

Relevant Links

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 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.

.. card-carousel:: 2

   .. card:: Source code
      :link: https://github.com/tencent-quantum-lab/tensorcircuit
      :shadow: md

      GitHub


   .. card:: Documentation
      :link: https://tensorcircuit.readthedocs.io
      :shadow: md

      Readthedocs


   .. card:: Whitepaper
      :link: https://quantum-journal.org/papers/q-2023-02-02-912/
      :shadow: md

      *Quantum* journal


   .. card:: Issue Tracker
      :link: https://github.com/tencent-quantum-lab/tensorcircuit/issues
      :shadow: md

      GitHub Issues


   .. card:: Forum
      :link: https://github.com/tencent-quantum-lab/tensorcircuit/discussions
      :shadow: md

      GitHub Discussions


   .. card:: PyPI
      :link:  https://pypi.org/project/tensorcircuit
      :shadow: md

      ``pip install``


   .. card:: DockerHub
      :link: https://hub.docker.com/repository/docker/tensorcircuit/tensorcircuit
      :shadow: md

      ``docker pull``


   .. card:: Application
      :link: https://github.com/tencent-quantum-lab/tensorcircuit#research-and-applications
      :shadow: md

      Research using TC


   .. card:: Cloud
      :link: https://quantum.tencent.com/cloud

      Tencent Quantum Cloud

Unified Quantum Programming

TensorCircuit is unifying infrastructures and interfaces for quantum computing.

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   :margin: 0
   :padding: 0
   :gutter: 2

   .. grid-item-card:: Unified Backends
      :columns: 12 6 3 3
      :shadow: md

      Jax/TensorFlow/PyTorch/Numpy/Cupy

   .. grid-item-card:: Unified Devices
      :columns: 12 6 3 3
      :shadow: md

      CPU/GPU/TPU

   .. grid-item-card:: Unified Providers
      :columns: 12 6 3 3
      :shadow: md

      QPUs from different vendors

   .. grid-item-card:: Unified Resources
      :columns: 12 6 3 3
      :shadow: md

      local/cloud/HPC

.. grid:: 1 2 4 4
   :margin: 0
   :padding: 0
   :gutter: 2

   .. grid-item-card:: Unified Interfaces
      :columns: 12 6 3 3
      :shadow: md

      numerical sim/hardware exp

   .. grid-item-card:: Unified Engines
      :columns: 12 6 3 3
      :shadow: md

      ideal/noisy/approximate simulation

   .. grid-item-card:: Unified Representations
      :columns: 12 6 3 3
      :shadow: md

      from/to_IR/qiskit/openqasm/json

   .. grid-item-card:: Unified Pipelines
      :columns: 12 6 3 3
      :shadow: md

      stateless functional programming/stateful ML models

Reference Documentation

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

Tutorials

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

API References

.. toctree::
   :maxdepth: 2

   modules.rst

Indices and Tables

• :ref:`genindex`
• :ref:`modindex`
• :ref:`search`