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, using the near-term quantum devices directly.

Best of both worlds

Support for hybrid quantum and classical models, and compatible with existing machine learning libraries. Quantum circuits can be set up to interface with either NumPy, PyTorch, or TensorFlow, allowing hybrid CPU-GPU-QPU computations.

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, Rigetti Forest, ProjectQ, Qiskit, and IBM Q.

PennyLane supports a growing ecosystem, including a wide range of quantum hardware and machine learning libraries
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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, the Rigetti Aspen-1 QPU, provided by the PennyLane-Forest 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
  • PennyLane v0.3 now supports integration with PyTorch and TensorFlow, allowing for CPU-GPU-QPU hybrid computations. Check out our blog post, and download the latest release now to give it a spin!
  • The PennyLane-Forest plugin is now available! Use it to run hybrid quantum-classical machine learning algorithms directly on the Rigetti Aspen-1 QPU, increasing the number of near-term quantum hardware devices PennyLane is compatible with.
  • 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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