PennyLane: Automatic differentiation of hybrid quantum-classical computations

11/12/2018 ∙ by Ville Bergholm, et al. ∙ 0

PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantum-classical computations. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for StrawberryFields and ProjectQ (including a IBMQE device interface). PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.



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Code Repositories


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

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Contains the PennyLane ProjectQ plugin. This plugin provides three devices to work with PennyLane - the ProjectQ IBM device, the ProjectQ quantum simulator, and the ProjectQ classical simulator.

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Contains the PennyLane Strawberry Fields plugin. This plugin allows two devices to work with OpenQML - the Strawberry Fields Fock backend, and the Strawberry Fields Gaussian backend.

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