PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry.
Create meaningful quantum algorithms, from inspiration to implementation.
*Inspiration to implementation, quickly.*Quantum computing can be complex — PennyLane makes it natural. Leverage the world’s largest library of
research demos,interactive tutorials, and state-of-the-art components to build algorithms inquantum chemistry, quantum information,optimization, andquantum 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 estimationand theCatalyst compiler. Scale up your workflows with thehigh-performance Lightning simulatorson GPUs, supercomputers, and the cloud. - *Hardware agnostic, hardware ready.*PennyLane integrates with a wide range of
quantum hardware devices. Whether superconducting qubits, trapped ion systems, neutral atoms, or photonics, PennyLane provides the tools toestimate resourcesandcompile circuitsspecifically for thehardware devicesof today—and tomorrow! - *Participate, collaborate, innovate.*PennyLane is the world’s most
active quantum community. You're part of a global network ofresearchers,developers, andeducatorsactively defining the frontier of quantum computing. Whether quantum is your day job or you’re getting your first taste at ahackathon, you’re backed by themost responsive communityin the field.
For more details and additional features, please see the PennyLane website and our most recent release notes.
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, where there is also a detailed description about PennyLane Docker support. See description here for more information.
Get up and running quickly with PennyLane by following our interactive tutorials and quickstart guide, 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 demosLearn Quantum Programmingwith theCodebookandCoding ChallengesPennyLane Discussion Forum
You can also check out our documentation, and detailed developer guides.
Take a deeper dive into quantum computing by exploring quantum computing research with the PennyLane Demos—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.
We welcome contributions—simply fork the PennyLane repository, and then make a pull request 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 and our Development guide for more details.
Source Code:https://github.com/PennyLaneAI/pennylane** Issue Tracker:**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 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.
PennyLane is the work of many contributors.
If you are doing research using PennyLane, please cite our paper:
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.