# tensorflow-quantum **Repository Path**: mirrors/tensorflow-quantum ## Basic Information - **Project Name**: tensorflow-quantum - **Description**: TensorFlow Quantum（TFQ）是用于混合量子经典机器学习的 Python 框架，主要致力于建模量子数据 - **Primary Language**: C/C++ - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: https://www.oschina.net/p/tensorflow-quantum - **GVP Project**: No ## Statistics - **Stars**: 6 - **Forks**: 6 - **Created**: 2020-03-11 - **Last Updated**: 2025-09-13 ## Categories & Tags **Categories**: quantum **Tags**: None ## README
TensorFlow Quantum logo High-performance Python framework for hybrid quantum-classical machine learning [![Licensed under the Apache 2.0 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) [![Compatible with Python versions 3.10 and higher](https://img.shields.io/badge/Python-3.10+-fcbc2c.svg?style=flat-square&logo=python&logoColor=white)](https://www.python.org/downloads/) [![TensorFlow Quantum project on 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) [Features](#features) – [Installation](#installation) – [Quick Start](#quick-start) – [Documentation](#documentation) – [Getting help](#getting-help) – [Citing TFQ](#citing-tensorflow-quantum) – [Contact](#contact)
## Features [TensorFlow Quantum](https://www.tensorflow.org/quantum) (TFQ) is a Python framework for hybrid quantum-classical machine learning focused on modeling quantum data. It enables quantum algorithms researchers and machine learning applications researchers to explore computing workflows that leverage Google’s quantum computing offerings – all from within the powerful [TensorFlow](https://tensorflow.org) ecosystem. * Integrates with [Cirq](https://github.com/quantumlib/Cirq) for writing quantum circuit definitions * Integrates with [qsim](https://github.com/quantumlib/qsim) for running quantum circuit simulations * Uses [Keras](https://keras.io) 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 1st-class citizens in the TF compute graph * Harnesses TensorFlow’s computational machinery to provide exceptional performance and scalability ## Motivation TensorFlow Quantum 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. With this connection, we hope to unlock new and exciting paths for quantum computing research that would not have otherwise been possible. Thanks to its power and scalability, TensorFlow Quantum has already been instrumental in enabling ground-breaking research in QML. It empowers researchers to pursue questions whose answers can only be obtained through fast simulation of many millions of moderately-sized circuits. ## Installation Please see the [installation instructions](https://www.tensorflow.org/quantum/install) in the documentation. ## Quick start [Guides and tutorials for TensorFlow Quantum](https://tensorflow.org/quantum/overview) are available online at the TensorFlow.org web site. ## Documentation [Documentation for TensorFlow Quantum](https://tensorflow.org/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](https://github.com/tensorflow/quantum/tree/master/docs/tutorials) ## Getting help Please report bugs or feature requests using the [TensorFlow Quantum issue tracker](https://github.com/tensorflow/quantum/issues) on GitHub. There is also a [Stack Overflow tag for TensorFlow Quantum](https://stackoverflow.com/questions/tagged/tensorflow-quantum) that you can use for more general TFQ-related discussions. ## Citing TensorFlow Quantum When publishing articles or otherwise writing about TensorFlow Quantum, please cite the paper ["TensorFlow Quantum: A Software Framework for Quantum Machine Learning" (2020)](https://arxiv.org/abs/2003.02989) and include information about the version of TFQ you are using. ```bibtex @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}, } ``` ## Contact For any questions or concerns not addressed here, please email quantum-oss-maintainers@google.com. ## Disclaimer This is not an officially supported Google product. This project is not eligible for the [Google Open Source Software Vulnerability Rewards Program](https://bughunters.google.com/open-source-security). Copyright 2020 Google LLC.
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