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TensorFlow Quantum (TFQ) is a Python framework for hybrid quantum-classical machine learning focused on modeling quantum data. It provides users with the tools they need to interleave quantum algorithms and logic designed in Cirq with the powerful and performant ML tools from TensorFlow. Here are some of TFQ's features:

• Integrates with Cirq for writing quantum circuit definitions
• Integrates with qsim for running quantum circuit simulations
• Uses Keras to provide high-level abstractions for quantum machine learning constructs
• Provides an extensible system for automatic differentiation of quantum circuits
• Offers many methods for computing gradients, including parameter shift and adjoint methods
• Implements operations as C++ TensorFlow Ops, making them 1^st-class citizens in the TF compute graph
• Harnesses TensorFlow’s computational machinery to provide exceptional performance and scalability

TensorFlow Quantum empowers quantum algorithms and machine learning researchers to pursue questions whose answers can only be obtained through fast simulation of many millions of moderately-sized circuits. It has already been instrumental in enabling ground-breaking research in QML by providing a seamless workflow for leveraging Google’s quantum computing offerings.

Please see the installation instructions in the documentation.

Compatibility: At this time, TensorFlow Quantum is built and tested on Linux with the following systems and software:

• Python 3.10–3.12
• TensorFlow 2.19.1
• TF-Keras 2.19.0
• NumPy 2.0
• Cirq 1.5.0

Guides and tutorials for TensorFlow Quantum are available online at the TensorFlow.org web site.

Documentation for TensorFlow Quantum, including tutorials and API documentation, can be found online at the TensorFlow.org web site.

All of the examples can be found in GitHub in the form of Python notebook tutorials

Please report bugs or feature requests using the TensorFlow Quantum issue tracker on GitHub.

There is also a Stack Overflow tag for TensorFlow Quantum that you can use for more general TFQ-related discussions.

When publishing articles or otherwise writing about TensorFlow Quantum, please cite the paper "TensorFlow Quantum: A Software Framework for Quantum Machine Learning" (2020) and include information about the version of TFQ you are using.

@misc{broughton2021tensorflowquantum,
      title={TensorFlow Quantum: A Software Framework for Quantum Machine Learning},
      author={Michael Broughton and Guillaume Verdon and Trevor McCourt
      and Antonio J. Martinez and Jae Hyeon Yoo and Sergei V. Isakov
      and Philip Massey and Ramin Halavati and Murphy Yuezhen Niu
      and Alexander Zlokapa and Evan Peters and Owen Lockwood and Andrea Skolik
      and Sofiene Jerbi and Vedran Dunjko and Martin Leib and Michael Streif
      and David Von Dollen and Hongxiang Chen and Shuxiang Cao and Roeland Wiersema
      and Hsin-Yuan Huang and Jarrod R. McClean and Ryan Babbush and Sergio Boixo
      and Dave Bacon and Alan K. Ho and Hartmut Neven and Masoud Mohseni},
      year={2021},
      eprint={2003.02989},
      archivePrefix={arXiv},
      primaryClass={quant-ph},
      doi={10.48550/arXiv.2003.02989},
      url={https://arxiv.org/abs/2003.02989},
}

For any questions or concerns not addressed here, please email [email protected].

This is not an officially supported Google product. This project is not eligible for the Google Open Source Software Vulnerability Rewards Program.

Copyright 2020 Google LLC.