The TensorFlow of quantum computing: built-in automatic differentiation of quantum circuits, using the near-term quantum devices directly.
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.
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.