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‎README.md

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![TensorFlow Quantum](./docs/images/logo/tf_quantum_circle.jpg)
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<!-- H1 title omitted because our logo acts as the title. -->
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<div align="center">
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---
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<img width="450px" alt="TensorFlow Quantum logo"
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src="docs/images/logo/tf_quantum1.svg">
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[TensorFlow Quantum](https://www.tensorflow.org/quantum) (TFQ) is a Python
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framework for hybrid quantum-classical machine learning that is primarily
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focused on modeling quantum data. TFQ is an application framework developed to
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allow quantum algorithms researchers and machine learning applications
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researchers to explore computing workflows that leverage Google’s quantum
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computing offerings, all from within TensorFlow.
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High-performance Python framework for hybrid quantum-classical machine learning
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[![Licensed under the Apache 2.0
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license](https://img.shields.io/badge/License-Apache%202.0-3c60b1.svg?logo=opensourceinitiative&logoColor=white&style=flat-square)](https://github.com/tensorflow/quantum/blob/master/LICENSE)
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[![Compatible with Python versions 3.10 and
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higher](https://img.shields.io/badge/Python-3.10+-fcbc2c.svg?style=flat-square&logo=python&logoColor=white)](https://www.python.org/downloads/)
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[![TensorFlow Quantum project on
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PyPI](https://img.shields.io/pypi/v/TensorFlow_Quantum.svg?logo=python&logoColor=white&label=PyPI&style=flat-square&color=fcbc2c)](https://pypi.org/project/tensorflow-quantum)
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## Motivation
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[Features](#features) &ndash;
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[Installation](#installation) &ndash;
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[Quick Start](#quick-start) &ndash;
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[Documentation](#documentation) &ndash;
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[Getting help](#getting-help) &ndash;
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[Citing TFQ](#citing-tensorflow-quantum) &ndash;
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[Contact](#contact)
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</div>
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## Features
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Quantum computing at Google has hit an exciting milestone with the achievement
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of [Quantum Supremacy](https://www.nature.com/articles/s41586-019-1666-5).
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In the wake of this demonstration, Google is now turning its attention to
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developing and implementing new algorithms to run on its Quantum Computer
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that have real world [applications](https://ai.googleblog.com/2019/10/quantum-supremacy-using-programmable.html).
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[TensorFlow Quantum](https://www.tensorflow.org/quantum) (TFQ) is a Python
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framework for hybrid quantum-classical machine learning focused on modeling
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quantum data. It enables quantum algorithms researchers and machine learning
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applications researchers to explore computing workflows that leverage Google’s
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quantum computing offerings – all from within the powerful
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[TensorFlow](https://tensorflow.org) ecosystem.
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* Integrates with [Cirq](https://github.com/quantumlib/Cirq) for writing
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quantum circuit definitions
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* Integrates with [qsim](https://github.com/quantumlib/qsim) for running
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quantum circuit simulations
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* Uses [Keras](https://keras.io) to provide high-level abstractions for
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quantum machine learning constructs
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* Provides an extensible system for automatic differentiation of quantum
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circuits
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* Offers many methods for computing gradients, including parameter shift and
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adjoint methods
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* Implements operations as C++ TensorFlow Ops, making them 1<sup>st</sup>-class
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citizens in the TF compute graph
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* Harnesses TensorFlow’s computational machinery to provide exceptional
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performance and scalability
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To provide users with the tools they need to program and simulate a quantum
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computer, Google is working on [Cirq](https://github.com/quantumlib/Cirq). Cirq
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is designed for quantum computing researchers who are interested in running and
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designing algorithms that leverage existing (imperfect) quantum computers.
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## Motivation
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TensorFlow Quantum provides users with the tools they need to interleave quantum
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algorithms and logic designed in Cirq with the powerful and performant ML tools
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from TensorFlow. With this connection we hope to unlock new and exciting paths
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for Quantum Computing research that would not have otherwise been possible.
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from TensorFlow. With this connection, we hope to unlock new and exciting paths
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for quantum computing research that would not have otherwise been possible.
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Thanks to its power and scalability, TensorFlow Quantum has already been
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instrumental in enabling ground-breaking research in QML. It empowers
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researchers to pursue questions whose answers can only be obtained through fast
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simulation of many millions of moderately-sized circuits.
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## Installation
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See the [installation instructions](https://github.com/tensorflow/quantum/blob/master/docs/install.md).
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Please see the [installation
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instructions](https://www.tensorflow.org/quantum/install) in the documentation.
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## Quick start
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[Guides and tutorials for TensorFlow
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Quantum](https://tensorflow.org/quantum/overview) are available online at the
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TensorFlow.org web site.
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## Documentation
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## Examples
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[Documentation for TensorFlow Quantum](https://tensorflow.org/quantum),
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including tutorials and API documentation, can be found online at the
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TensorFlow.org web site.
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All of our examples can be found here in the form of
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[Python notebook tutorials](https://github.com/tensorflow/quantum/tree/master/docs/tutorials)
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All of the examples can be found in GitHub in the form of [Python notebook
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tutorials](https://github.com/tensorflow/quantum/tree/master/docs/tutorials)
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## Getting help
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## Report issues
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Please report bugs or feature requests using the [TensorFlow Quantum issue
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tracker](https://github.com/tensorflow/quantum/issues) on GitHub.
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Report bugs or feature requests using the
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[TensorFlow Quantum issue tracker](https://github.com/tensorflow/quantum/issues).
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There is also a [Stack Overflow tag for TensorFlow
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Quantum](https://stackoverflow.com/questions/tagged/tensorflow-quantum) that you
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can use for more general TFQ-related discussions.
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We also have a [Stack Overflow tag](https://stackoverflow.com/questions/tagged/tensorflow-quantum)
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for more general TFQ related discussions.
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## Citing TensorFlow Quantum<a name="how-to-cite-tfq"></a><a name="how-to-cite"></a>
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In the meantime check out the [install instructions](./docs/install.md) to get
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the experimental code running!
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When publishing articles or otherwise writing about TensorFlow Quantum, please
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cite the paper ["TensorFlow Quantum: A Software Framework for Quantum Machine
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Learning" (2020)](https://arxiv.org/abs/2003.02989) and include information
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about the version of TFQ you are using.
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```bibtex
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@misc{broughton2021tensorflowquantum,
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title={TensorFlow Quantum: A Software Framework for Quantum Machine Learning},
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author={Michael Broughton and Guillaume Verdon and Trevor McCourt
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and Antonio J. Martinez and Jae Hyeon Yoo and Sergei V. Isakov
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and Philip Massey and Ramin Halavati and Murphy Yuezhen Niu
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and Alexander Zlokapa and Evan Peters and Owen Lockwood and Andrea Skolik
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and Sofiene Jerbi and Vedran Dunjko and Martin Leib and Michael Streif
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and David Von Dollen and Hongxiang Chen and Shuxiang Cao and Roeland Wiersema
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and Hsin-Yuan Huang and Jarrod R. McClean and Ryan Babbush and Sergio Boixo
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and Dave Bacon and Alan K. Ho and Hartmut Neven and Masoud Mohseni},
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year={2021},
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eprint={2003.02989},
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archivePrefix={arXiv},
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primaryClass={quant-ph},
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doi={10.48550/arXiv.2003.02989},
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url={https://arxiv.org/abs/2003.02989},
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}
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```
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## Contributing
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## Contact
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We are eager to collaborate with you! TensorFlow Quantum is still a very young code base,
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if you have ideas for features that you would like added feel free to check out our
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[Contributor Guidelines](https://github.com/tensorflow/quantum/blob/master/CONTRIBUTING.md)
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to get started.
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For any questions or concerns not addressed here, please email
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[email protected].
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## Disclaimer
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## References
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This is not an officially supported Google product. This project is not eligible
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for the [Google Open Source Software Vulnerability Rewards
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Program](https://bughunters.google.com/open-source-security).
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If you use TensorFlow Quantum in your research, please cite:
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Copyright 2020 Google LLC.
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TensorFlow Quantum: A Software Framework for Quantum Machine Learning
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[arXiv:2003.02989, 2020](https://arxiv.org/abs/2003.02989).
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<div align="center">
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<a href="https://quantumai.google">
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<img width="15%" alt="Google Quantum AI"
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src="docs/images/quantum-ai-vertical.svg">
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</a>
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</div>

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