PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry.

The definitive open-source framework for quantum programming. Built by researchers, for research.

Key Features

• Program quantum computers. Build quantum circuits with a wide range of state preparations, gates, and measurements. Run on high-performance simulators or various hardware devices, with advanced features like mid-circuit measurements and error mitigation.

• Master quantum algorithms. From NISQ to fault-tolerant quantum computing, unlock algorithms for research and application. Analyze performance, visualize circuits, and access tools for quantum chemistry and algorithm development.

• Machine learning with quantum hardware and simulators. 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.

• Quantum datasets. Access high-quality, pre-simulated datasets to decrease time-to-research and accelerate algorithm development. Browse the datasets or contribute your own data.

• Compilation and performance. Experimental support for just-in-time compilation. Compile your entire hybrid workflow, with support for advanced features such as adaptive circuits, real-time measurement feedback, and unbounded loops. See Catalyst for more details.

For more details and additional features, please see the PennyLane website.

Installation

PennyLane requires Python version 3.10 and above. Installation of PennyLane, as well as all dependencies, can be done using pip:

python -m pip install pennylane

Docker support

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.

Getting started

Get up and running quickly with PennyLane by following our quickstart guide, designed to introduce key features and help you start building quantum circuits right away.

Whether 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:

Key Resources:

• Research-oriented Demos
• Learn Quantum Programming with the Codebook and Coding Challenges
• Frequently Asked Questions
• Glossary
• Videos

You can also check out our documentation for quickstart guides to using PennyLane, and detailed developer guides on how to write your own PennyLane-compatible quantum device.

Demos

Take a deeper dive into quantum computing by exploring cutting-edge algorithms using PennyLane and quantum hardware. Explore PennyLane demos.

If you would like to contribute your own demo, see our demo submission guide.

Research Applications

PennyLane 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:

• Quantum Computing: Fast quantum circuit cutting with randomized measurements

• Quantum Machine Learning: Better than classical? The subtle art of benchmarking quantum machine learning models

• Quantum Chemistry: Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization

Impactful research drives PennyLane. Let us know what features you need for your research on GitHub or on our website.

Contributing to PennyLane

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. All users who contribute significantly to the code (new plugins, new functionality, etc.) will be listed on the PennyLane arXiv paper.

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.

Support

• 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.

Authors

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

License

PennyLane is free and open source, released under the Apache License, Version 2.0.