A cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations


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The TensorFlow of quantum computing: built-in automatic differentiation of quantum circuits.

Best of both worlds

Support for hybrid quantum and classical models, with built-in optimization and machine learning tools.

Device independent

The same quantum circuit model can be run on different devices. Install plugins to run your computational circuits on more devices, including Strawberry Fields, ProjectQ, Qiskit, and IBM Q.

Comes with a growing plugin ecosystem, supporting a wide range of near-term quantum hardware
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Getting started

To get PennyLane installed and running on your system, begin at the download and installation guide. Then, familiarize yourself with the PennyLane's key concepts for machine learning on quantum circuits. For getting started with PennyLane, check out our basic qubit rotation, and Gaussian transformation tutorials, before continuing on to explore hybrid quantum optimization. More advanced tutorials include supervised learning, building quantum GANs (QGANs), and quantum classifiers.
Next, play around with the numerous devices and plugins available for running your hybrid models; these include Strawberry Fields, provided by the PennyLane-SF plugin, and the IBM QX4 quantum chip, provided by the PennyLane-PQ and PennyLane-qiskit plugins. Finally, detailed documentation on the PennyLane interface and API is provided. Look there for full details on available quantum operations and expectations, and detailed guides on how to write your own PennyLane compatible quantum device.
If you have any questions along the way, head over to our discussion forum to chat with the PennyLane community.

News and annoucements
  • Announcing the Xanadu Quantum Software Competition. There are three awards — education, software, and research — with multiple prizes of CAD$1000 on offer. Submission close 30th August 2019. Go to the competition website, or see our latest blog post, for more information.
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