{"id":13471619,"url":"https://github.com/PennyLaneAI/pennylane","last_synced_at":"2025-03-26T13:31:18.809Z","repository":{"id":36980293,"uuid":"129936360","full_name":"PennyLaneAI/pennylane","owner":"PennyLaneAI","description":"PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. 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SDKs","其他_机器学习与深度学习","Other Machine Learning Applications","Python","Frameworks \u0026 Libraries","Quantum Computing","💻 Computing Frameworks"],"sub_categories":["Others","Quantum Machine Learning \u0026 Hybrid Frameworks","Specialized Frameworks"],"readme":"\u003cp align=\"center\"\u003e\r\n  \u003c!-- Tests (GitHub actions) --\u003e\r\n  \u003ca href=\"https://github.com/PennyLaneAI/pennylane/actions?query=workflow%3ATests\"\u003e\r\n    \u003cimg src=\"https://img.shields.io/github/actions/workflow/status/PennyLaneAI/PennyLane/tests.yml?branch=master\u0026style=flat-square\" /\u003e\r\n  \u003c/a\u003e\r\n  \u003c!-- CodeCov --\u003e\r\n  \u003ca href=\"https://codecov.io/gh/PennyLaneAI/pennylane\"\u003e\r\n    \u003cimg src=\"https://img.shields.io/codecov/c/github/PennyLaneAI/pennylane/master.svg?logo=codecov\u0026style=flat-square\" /\u003e\r\n  \u003c/a\u003e\r\n  \u003c!-- ReadTheDocs --\u003e\r\n  \u003ca href=\"https://docs.pennylane.ai/en/latest\"\u003e\r\n    \u003cimg src=\"https://readthedocs.com/projects/xanaduai-pennylane/badge/?version=latest\u0026style=flat-square\" /\u003e\r\n  \u003c/a\u003e\r\n  \u003c!-- PyPI --\u003e\r\n  \u003ca href=\"https://pypi.org/project/PennyLane\"\u003e\r\n    \u003cimg src=\"https://img.shields.io/pypi/v/PennyLane.svg?style=flat-square\" /\u003e\r\n  \u003c/a\u003e\r\n  \u003c!-- Forum --\u003e\r\n  \u003ca href=\"https://discuss.pennylane.ai\"\u003e\r\n    \u003cimg src=\"https://img.shields.io/discourse/https/discuss.pennylane.ai/posts.svg?logo=discourse\u0026style=flat-square\" /\u003e\r\n  \u003c/a\u003e\r\n  \u003c!-- License --\u003e\r\n  \u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\"\u003e\r\n    \u003cimg src=\"https://img.shields.io/pypi/l/PennyLane.svg?logo=apache\u0026style=flat-square\" /\u003e\r\n  \u003c/a\u003e\r\n\u003c/p\u003e\r\n\r\n\u003cp align=\"center\"\u003e\r\n  \u003ca href=\"https://pennylane.ai\"\u003ePennyLane\u003c/a\u003e is a cross-platform Python library for\r\n  \u003ca href=\"https://pennylane.ai/qml/quantum-computing/\"\u003equantum computing\u003c/a\u003e,\r\n  \u003ca href=\"https://pennylane.ai/qml/quantum-machine-learning/\"\u003equantum machine learning\u003c/a\u003e,\r\n  and\r\n  \u003ca href=\"https://pennylane.ai/qml/quantum-chemistry/\"\u003equantum chemistry\u003c/a\u003e.\r\n\u003c/p\u003e\r\n\r\n\u003cp align=\"center\"\u003e\r\n  The definitive open-source framework for quantum programming. Built by researchers, for research.\r\n  \u003cimg src=\"https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/readme/pl-logo-lightmode.png#gh-light-mode-only\" width=\"700px\"\u003e\r\n    \u003c!--\r\n    Use a relative import for the dark mode image. When loading on PyPI, this\r\n    will fail automatically and show nothing.\r\n    --\u003e\r\n    \u003cimg src=\"./doc/_static/readme/pl-logo-darkmode.png#gh-dark-mode-only\" width=\"700px\" onerror=\"this.style.display='none'\" alt=\"\"/\u003e\r\n\u003c/p\u003e\r\n\r\n## Key Features\r\n\r\n\u003cimg src=\"https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/code.png\" width=\"400px\" align=\"right\"\u003e\r\n\r\n- \u003cstrong\u003e*Program quantum computers*\u003c/strong\u003e. Build quantum circuits with a wide range of state preparations, gates, and measurements. Run on [high-performance simulators](https://pennylane.ai/performance/) or [various hardware devices](https://pennylane.ai/plugins/), with advanced features like mid-circuit measurements and error mitigation.\r\n\r\n- \u003cstrong\u003e*Master quantum algorithms*\u003c/strong\u003e. From NISQ to fault-tolerant quantum computing, unlock algorithms for research and application. Analyze performance, visualize circuits, and access tools for [quantum chemistry](https://docs.pennylane.ai/en/stable/introduction/chemistry.html) and [algorithm development](https://pennylane.ai/search/?contentType=DEMO\u0026categories=algorithms\u0026sort=publication_date).\r\n\r\n- \u003cstrong\u003e*Machine learning with quantum hardware and simulators*\u003c/strong\u003e. Integrate with **PyTorch**, **TensorFlow**, **JAX**, **Keras**, or **NumPy** to define and train hybrid models using quantum-aware optimizers and hardware-compatible gradients for advanced research tasks. [Quantum machine learning quickstart](https://docs.pennylane.ai/en/stable/introduction/interfaces.html).\r\n\r\n\r\n- \u003cstrong\u003e*Quantum datasets*\u003c/strong\u003e. Access high-quality, pre-simulated datasets to decrease time-to-research and accelerate algorithm development. [Browse the datasets](https://pennylane.ai/datasets/) or contribute your own data.\r\n\r\n\r\n- \u003cstrong\u003e*Compilation and performance*\u003c/strong\u003e. Experimental support for just-in-time\r\n  compilation. Compile your entire hybrid workflow, with support for \r\n  advanced features such as adaptive circuits, real-time measurement \r\n  feedback, and unbounded loops. See\r\n  [Catalyst](https://github.com/pennylaneai/catalyst) for more details.\r\n\r\nFor more details and additional features, please see the [PennyLane website](https://pennylane.ai/features/).\r\n\r\n## Installation\r\n\r\nPennyLane requires Python version 3.10 and above. Installation of PennyLane, as well as all\r\ndependencies, can be done using pip:\r\n\r\n```console\r\npython -m pip install pennylane\r\n```\r\n\r\n## Docker support\r\n\r\nDocker images are found on the [PennyLane Docker Hub page](https://hub.docker.com/u/pennylaneai), where there is also a detailed description about PennyLane Docker support. [See description here](https://docs.pennylane.ai/projects/lightning/en/stable/dev/docker.html) for more information.\r\n\r\n## Getting started\r\n\r\nGet up and running quickly with PennyLane by following our [quickstart guide](https://docs.pennylane.ai/en/stable/introduction/pennylane.html), designed to introduce key features and help you start building quantum circuits right away.\r\n\r\nWhether you're exploring quantum machine learning (QML), quantum computing, or quantum chemistry, PennyLane offers a wide range of tools and resources to support your research:\r\n\r\n\u003cimg src=\"./doc/_static/readme/research.png\" align=\"right\" width=\"350px\"\u003e\r\n\r\n### Key Resources:\r\n\r\n* [Research-oriented Demos](https://pennylane.ai/qml/demonstrations.html)\r\n* [Learn Quantum Programming](https://pennylane.ai/qml/) with the [Codebook](https://pennylane.ai/codebook/) and [Coding Challenges](https://pennylane.ai/challenges/)\r\n* [Frequently Asked Questions](https://pennylane.ai/faq.html)\r\n* [Glossary](https://pennylane.ai/qml/glossary.html)\r\n* [Videos](https://pennylane.ai/qml/videos.html)\r\n\r\n\r\nYou can also check out our [documentation](https://pennylane.readthedocs.io) for [quickstart\r\nguides](https://pennylane.readthedocs.io/en/stable/introduction/pennylane.html) to using PennyLane,\r\nand detailed developer guides on [how to write your\r\nown](https://pennylane.readthedocs.io/en/stable/development/plugins.html) PennyLane-compatible\r\nquantum device.\r\n\r\n## Demos\r\n\r\nTake a deeper dive into quantum computing by exploring cutting-edge algorithms using PennyLane and quantum hardware. [Explore PennyLane demos](https://pennylane.ai/qml/demonstrations.html).\r\n\r\n\u003ca href=\"https://pennylane.ai/qml/demonstrations\"\u003e\r\n  \u003cimg src=\"./doc/_static/readme/demos.png\" width=\"900px\"\u003e\r\n\u003c/a\u003e\r\n\r\nIf you would like to contribute your own demo, see our [demo submission\r\nguide](https://pennylane.ai/qml/demos_submission).\r\n\r\n## Research Applications\r\n\r\nPennyLane is at the forefront of research in quantum computing, quantum machine learning, and quantum chemistry. Explore how PennyLane is used for research in the following publications:\r\n\r\n- **Quantum Computing**: [Fast quantum circuit cutting with randomized measurements](https://quantum-journal.org/papers/q-2023-03-02-934/)\r\n\r\n- **Quantum Machine Learning**: [Better than classical? The subtle art of benchmarking quantum machine learning models](https://arxiv.org/abs/2403.07059)\r\n\r\n- **Quantum Chemistry**: [Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization](https://quantum-journal.org/papers/q-2024-06-13-1371/)\r\n\r\nImpactful research drives PennyLane. Let us know what features you need for your research on [GitHub](https://github.com/PennyLaneAI/pennylane/issues/new?assignees=\u0026labels=enhancement+%3Asparkles%3A\u0026projects=\u0026template=feature_request.yml) or on our [website](https://pennylane.ai/research).\r\n\r\n\r\n\r\n## Contributing to PennyLane\r\n\r\nWe welcome contributions—simply fork the PennyLane repository, and then make a [pull\r\nrequest](https://help.github.com/articles/about-pull-requests/) containing your contribution. All\r\ncontributors to PennyLane will be listed as authors on the releases. All users who contribute\r\nsignificantly to the code (new plugins, new functionality, etc.) will be listed on the PennyLane\r\narXiv paper.\r\n\r\nWe also encourage bug reports, suggestions for new features and enhancements, and even links to cool\r\nprojects or applications built on PennyLane.\r\n\r\nSee our [contributions\r\npage](https://github.com/PennyLaneAI/pennylane/blob/master/.github/CONTRIBUTING.md) and our\r\n[Development guide](https://pennylane.readthedocs.io/en/stable/development/guide.html) for more\r\ndetails.\r\n\r\n## Support\r\n\r\n- **Source Code:** https://github.com/PennyLaneAI/pennylane\r\n- **Issue Tracker:** https://github.com/PennyLaneAI/pennylane/issues\r\n\r\nIf you are having issues, please let us know by posting the issue on our GitHub issue tracker.\r\n\r\nJoin the [PennyLane Discussion Forum](https://discuss.pennylane.ai/) to connect with the quantum community, get support, and engage directly with our team. It’s the perfect place to share ideas, ask questions, and collaborate with fellow researchers and developers!\r\n\r\nNote that we are committed to providing a friendly, safe, and welcoming environment for all.\r\nPlease read and respect the [Code of Conduct](.github/CODE_OF_CONDUCT.md).\r\n\r\n## Authors\r\n\r\nPennyLane is the work of [many contributors](https://github.com/PennyLaneAI/pennylane/graphs/contributors).\r\n\r\nIf you are doing research using PennyLane, please cite [our paper](https://arxiv.org/abs/1811.04968):\r\n\r\n\u003e Ville Bergholm et al. *PennyLane: Automatic differentiation of hybrid quantum-classical\r\n\u003e computations.* 2018. arXiv:1811.04968\r\n\r\n## License\r\n\r\nPennyLane is **free** and **open source**, released under the Apache License, Version 2.0.\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FPennyLaneAI%2Fpennylane","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FPennyLaneAI%2Fpennylane","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FPennyLaneAI%2Fpennylane/lists"}