PennyLane is an open-source quantum software platform for quantum

PennyLane, an open-source quantum software platform, enables users to build quantum algorithms for quantum computing, machine learning, and chemistry. It offers high-performance simulators, hardware integration, and a large community for researchers and developers.

PennyLane https://pennylane.ai is an open-source quantum software platform for quantum computing https://pennylane.ai/qml/quantum-computing/ , quantum machine learning https://pennylane.ai/topics/quantum-machine-learning , and quantum chemistry https://pennylane.ai/topics/hamiltonian-simulation . Create meaningful quantum algorithms, from inspiration to implementation. https://raw.githubusercontent.com/PennyLaneAI/pennylane/main/doc/ static/readme/pl-logo-lightmode.png gh-light-mode-only /PennyLaneAI/pennylane/blob/main/doc/ static/readme/pl-logo-darkmode.png gh-dark-mode-only - Inspiration to implementation, quickly. Quantum computing can be complex — PennyLane makes it natural. Leverage the world’s largest library of research demos https://pennylane.ai/qml/demonstrations , interactive tutorials https://pennylane.ai/codebook/ , and state-of-the-art components to build algorithms in quantum chemistry https://docs.pennylane.ai/en/stable/introduction/chemistry.html , quantum information, optimization https://pennylane.ai/qml/demos/tutorial dqi , and quantum machine learning https://pennylane.ai/topics/quantum-machine-learning . - Fast where it matters. Scalable where it counts. Whether executing, compiling, or analyzing, PennyLane is fast. Unlock production-grade performance with industrial resource estimation https://pennylane.ai/qml/demos/re how to use pennylane for resource estimation and the Catalyst compiler https://github.com/PennyLaneAI/Catalyst . Scale up your workflows with the high-performance Lightning simulators https://pennylane.ai/performance on GPUs, supercomputers, and the cloud. - Hardware agnostic, hardware ready. PennyLane integrates with a wide range of quantum hardware devices https://pennylane.ai/devices . Whether superconducting qubits, trapped ion systems, neutral atoms, or photonics, PennyLane provides the tools to estimate resources https://pennylane.ai/qml/demos/re how to use pennylane for resource estimation and compile circuits https://pennylane.ai/topics/quantum-compilation specifically for the hardware devices https://pennylane.ai/topics/quantum-hardware of today—and tomorrow - Participate, collaborate, innovate. PennyLane is the world’s most active quantum community https://pennylane.ai/get-involved . You're part of a global network of researchers https://pennylane.ai/research , developers https://pennylane.ai/features , and educators https://pennylane.ai/education actively defining the frontier of quantum computing. Whether quantum is your day job or you’re getting your first taste at a hackathon https://pennylane.ai/challenges , you’re backed by the most responsive community https://discuss.pennylane.ai in the field. For more details and additional features, please see the PennyLane website https://pennylane.ai/features/ and our most recent release notes https://docs.pennylane.ai/en/stable/development/release notes.html . PennyLane requires Python version 3.11 and above. Installation of PennyLane, as well as all dependencies, can be done using pip: python -m pip install pennylane Docker 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. Get up and running quickly with PennyLane by following our interactive tutorials https://pennylane.ai/codebook/pennylane-fundamentals and quickstart guide https://pennylane.ai/features , designed to introduce key features and help you start building quantum circuits right away. Whether you're exploring quantum machine learning, quantum computing, or quantum chemistry, PennyLane offers a wide range of tools and resources to support your research. Library of research demos https://pennylane.ai/qml/demonstrations Learn Quantum Programming https://pennylane.ai/qml/ with the Codebook https://pennylane.ai/codebook/ and Coding Challenges https://pennylane.ai/challenges/ PennyLane Discussion Forum https://discuss.pennylane.ai You can also check out our documentation https://pennylane.readthedocs.io , and detailed developer guides https://docs.pennylane.ai/en/stable/development/guide.html . Take a deeper dive into quantum computing by exploring quantum computing research with the PennyLane Demos https://pennylane.ai/qml/demonstrations —covering fundamental quantum concepts alongside the latest quantum algorithm research results. If you would like to contribute your own demo, see our demo submission guide https://pennylane.ai/qml/demos submission . We welcome contributions—simply fork the PennyLane repository, and then make a pull request https://help.github.com/articles/about-pull-requests/ containing your contribution. All contributors to PennyLane will be listed as authors on the releases. We also encourage bug reports, suggestions for new features and enhancements, and even links to cool projects or applications built on PennyLane. See our contributions page https://github.com/PennyLaneAI/pennylane/blob/main/.github/CONTRIBUTING.md and our Development guide https://pennylane.readthedocs.io/en/stable/development/guide.html for more details. Source Code: https://github.com/PennyLaneAI/pennylane https://github.com/PennyLaneAI/pennylane Issue Tracker: https://github.com/PennyLaneAI/pennylane/issues https://github.com/PennyLaneAI/pennylane/issues If you are having issues, please let us know by posting the issue on our GitHub issue tracker. Join 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 Note that we are committed to providing a friendly, safe, and welcoming environment for all. Please read and respect the Code of Conduct /PennyLaneAI/pennylane/blob/main/.github/CODE OF CONDUCT.md . PennyLane is the work of many contributors https://github.com/PennyLaneAI/pennylane/graphs/contributors . If you are doing research using PennyLane, please cite our paper https://arxiv.org/abs/1811.04968 : Ville Bergholm et al. PennyLane: Automatic differentiation of hybrid quantum-classical computations.2018. arXiv:1811.04968 PennyLane is free and open source , released under the Apache License, Version 2.0.