{"id":13536227,"url":"https://github.com/tensorflow/quantum","last_synced_at":"2025-12-29T07:41:35.095Z","repository":{"id":37510652,"uuid":"238772762","full_name":"tensorflow/quantum","owner":"tensorflow","description":"An open-source Python framework for hybrid quantum-classical machine 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H1 title omitted because our logo acts as the title. --\u003e\n\u003cdiv align=\"center\"\u003e\n\n\u003cimg width=\"450px\" alt=\"TensorFlow Quantum logo\"\nsrc=\"docs/images/logo/tf_quantum1.svg\"\u003e\n\nHigh-performance Python framework for hybrid quantum-classical machine learning\n\n[![Licensed under the Apache 2.0\nlicense](https://img.shields.io/badge/License-Apache%202.0-3c60b1.svg?logo=opensourceinitiative\u0026logoColor=white\u0026style=flat-square)](https://github.com/tensorflow/quantum/blob/master/LICENSE)\n[![Compatible with Python versions 3.10 and\nhigher](https://img.shields.io/badge/Python-3.10+-fcbc2c.svg?style=flat-square\u0026logo=python\u0026logoColor=white)](https://www.python.org/downloads/)\n[![TensorFlow Quantum project on\nPyPI](https://img.shields.io/pypi/v/TensorFlow_Quantum.svg?logo=python\u0026logoColor=white\u0026label=PyPI\u0026style=flat-square\u0026color=fcbc2c)](https://pypi.org/project/tensorflow-quantum)\n\n[Features](#features) \u0026ndash;\n[Installation](#installation) \u0026ndash;\n[Quick Start](#quick-start) \u0026ndash;\n[Documentation](#documentation) \u0026ndash;\n[Getting help](#getting-help) \u0026ndash;\n[Citing TFQ](#citing-tensorflow-quantum) \u0026ndash;\n[Contact](#contact)\n\n\u003c/div\u003e\n\n## Features\n\n[TensorFlow Quantum](https://www.tensorflow.org/quantum) (TFQ) is a Python\nframework for hybrid quantum-classical machine learning focused on modeling\nquantum data. It enables quantum algorithms researchers and machine learning\napplications researchers to explore computing workflows that leverage Google’s\nquantum computing offerings – all from within the powerful\n[TensorFlow](https://tensorflow.org) ecosystem.\n\n*   Integrates with [Cirq](https://github.com/quantumlib/Cirq) for writing\n    quantum circuit definitions\n*   Integrates with [qsim](https://github.com/quantumlib/qsim) for running\n    quantum circuit simulations\n*   Uses [Keras](https://keras.io) to provide high-level abstractions for\n    quantum machine learning constructs\n*   Provides an extensible system for automatic differentiation of quantum\n    circuits\n*   Offers many methods for computing gradients, including parameter shift and\n    adjoint methods\n*   Implements operations as C++ TensorFlow Ops, making them 1\u003csup\u003est\u003c/sup\u003e-class\n    citizens in the TF compute graph\n*   Harnesses TensorFlow’s computational machinery to provide exceptional\n    performance and scalability\n\n## Motivation\n\nTensorFlow Quantum provides users with the tools they need to interleave quantum\nalgorithms and logic designed in Cirq with the powerful and performant ML tools\nfrom TensorFlow. With this connection, we hope to unlock new and exciting paths\nfor quantum computing research that would not have otherwise been possible.\n\nThanks to its power and scalability, TensorFlow Quantum has already been\ninstrumental in enabling ground-breaking research in QML. It empowers\nresearchers to pursue questions whose answers can only be obtained through fast\nsimulation of many millions of moderately-sized circuits.\n\n## Installation\n\nPlease see the [installation\ninstructions](https://www.tensorflow.org/quantum/install) in the documentation.\n\n## Quick start\n\n[Guides and tutorials for TensorFlow\nQuantum](https://tensorflow.org/quantum/overview) are available online at the\nTensorFlow.org web site.\n\n## Documentation\n\n[Documentation for TensorFlow Quantum](https://tensorflow.org/quantum),\nincluding tutorials and API documentation, can be found online at the\nTensorFlow.org web site.\n\nAll of the examples can be found in GitHub in the form of [Python notebook\ntutorials](https://github.com/tensorflow/quantum/tree/master/docs/tutorials)\n\n## Getting help\n\nPlease report bugs or feature requests using the [TensorFlow Quantum issue\ntracker](https://github.com/tensorflow/quantum/issues) on GitHub.\n\nThere is also a [Stack Overflow tag for TensorFlow\nQuantum](https://stackoverflow.com/questions/tagged/tensorflow-quantum) that you\ncan use for more general TFQ-related discussions.\n\n## Citing TensorFlow Quantum\u003ca name=\"how-to-cite-tfq\"\u003e\u003c/a\u003e\u003ca name=\"how-to-cite\"\u003e\u003c/a\u003e\n\nWhen publishing articles or otherwise writing about TensorFlow Quantum, please\ncite the paper [\"TensorFlow Quantum: A Software Framework for Quantum Machine\nLearning\" (2020)](https://arxiv.org/abs/2003.02989) and include information\nabout the version of TFQ you are using.\n\n```bibtex\n@misc{broughton2021tensorflowquantum,\n      title={TensorFlow Quantum: A Software Framework for Quantum Machine Learning},\n      author={Michael Broughton and Guillaume Verdon and Trevor McCourt\n      and Antonio J. Martinez and Jae Hyeon Yoo and Sergei V. Isakov\n      and Philip Massey and Ramin Halavati and Murphy Yuezhen Niu\n      and Alexander Zlokapa and Evan Peters and Owen Lockwood and Andrea Skolik\n      and Sofiene Jerbi and Vedran Dunjko and Martin Leib and Michael Streif\n      and David Von Dollen and Hongxiang Chen and Shuxiang Cao and Roeland Wiersema\n      and Hsin-Yuan Huang and Jarrod R. McClean and Ryan Babbush and Sergio Boixo\n      and Dave Bacon and Alan K. Ho and Hartmut Neven and Masoud Mohseni},\n      year={2021},\n      eprint={2003.02989},\n      archivePrefix={arXiv},\n      primaryClass={quant-ph},\n      doi={10.48550/arXiv.2003.02989},\n      url={https://arxiv.org/abs/2003.02989},\n}\n```\n\n## Contact\n\nFor any questions or concerns not addressed here, please email\nquantum-oss-maintainers@google.com.\n\n## Disclaimer\n\nThis is not an officially supported Google product. This project is not eligible\nfor the [Google Open Source Software Vulnerability Rewards\nProgram](https://bughunters.google.com/open-source-security).\n\nCopyright 2020 Google LLC.\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://quantumai.google\"\u003e\n    \u003cimg width=\"15%\" alt=\"Google Quantum AI\"\n         src=\"docs/images/quantum-ai-vertical.svg\"\u003e\n  \u003c/a\u003e\n\u003c/div\u003e\n","funding_links":[],"categories":["Quantum Software Packages","Python"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftensorflow%2Fquantum","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftensorflow%2Fquantum","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftensorflow%2Fquantum/lists"}