This repository hosts a collection of demonstrations built with PennyLane, a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Built by researchers, for research. These demos showcase various applications and techniques in quantum machine learning (QML), quantum chemistry, and quantum computing, ranging from introductory concepts to advanced algorithms.
Explore the full collection of PennyLane demos, each available for download as a Jupyter notebook or Python script.
- Source Code: https://github.com/PennyLaneAI/QML
- Issue Tracker: https://github.com/PennyLaneAI/QML/issues
If you encounter any issues, have questions, or wish to suggest improvements, please report them on our GitHub issue tracker.
We are dedicated to fostering a friendly, safe, and welcoming environment for all contributors. Please review and adhere to our Code of Conduct.
The materials and demonstrations contained within this repository are free and open-source, released under the Apache License, Version 2.0.
Please note, the file custom_directives.py is available under the BSD 3-Clause License, with copyright © 2017, PyTorch contributors.