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TensorFlow Quantum (TFQ) is a Pythonframework for hybrid quantum-classical machine learning focused on modelingquantum data. It enables quantum algorithms researchers and machine learningapplications researchers to explore computing workflows that leverage Google’squantum computing offerings – all from within the powerfulTensorFlow ecosystem.

• Integrates withCirq for writingquantum circuit definitions
• Integrates withqsim for runningquantum circuit simulations
• UsesKeras to provide high-level abstractions forquantum machine learning constructs
• Provides an extensible system for automatic differentiation of quantumcircuits
• Offers many methods for computing gradients, including parameter shift andadjoint methods
• Implements operations as C++ TensorFlow Ops, making them 1^st-classcitizens in the TF compute graph
• Harnesses TensorFlow’s computational machinery to provide exceptionalperformance and scalability

TensorFlow Quantum provides users with the tools they need to interleave quantumalgorithms and logic designed in Cirq with the powerful and performant ML toolsfrom TensorFlow. With this connection, we hope to unlock new and exciting pathsfor quantum computing research that would not have otherwise been possible.

Thanks to its power and scalability, TensorFlow Quantum has already beeninstrumental in enabling ground-breaking research in QML. It empowersresearchers to pursue questions whose answers can only be obtained through fastsimulation of many millions of moderately-sized circuits.

Please see theinstallationinstructions in the documentation.

Guides and tutorials for TensorFlowQuantum are available online at theTensorFlow.org web site.

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

All of the examples can be found in GitHub in the form ofPython notebooktutorials

Please report bugs or feature requests using theTensorFlow Quantum issuetracker on GitHub.

There is also aStack Overflow tag for TensorFlowQuantum that youcan use for more general TFQ-related discussions.

When publishing articles or otherwise writing about TensorFlow Quantum, pleasecite the paper"TensorFlow Quantum: A Software Framework for Quantum MachineLearning" (2020) and include informationabout 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 emailquantum-oss-maintainers@google.com.

This is not an officially supported Google product. This project is not eligiblefor theGoogle Open Source Software Vulnerability RewardsProgram.

Copyright 2020 Google LLC.