PennyLane: Automatic differentiation of hybrid quantum-classical computations

TL;DR

PennyLane is a Python framework enabling automatic differentiation of hybrid quantum-classical computations, supporting various quantum hardware and classical ML libraries for diverse quantum algorithms.

Contribution

It introduces a unified architecture for differentiable programming of quantum computers, integrating quantum and classical optimization techniques.

Findings

Supports gradients of variational quantum circuits compatible with classical backpropagation.

Compatible with multiple quantum hardware providers and classical ML libraries.

Enables optimization of quantum algorithms like VQE and QAOA.

Abstract

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. 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 hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.