PennyLane: Automatic differentiation of hybrid quantum-classical computations

TLDR

PennyLane is a Python 3 framework enabling differentiable programming of quantum computers, useful for variational quantum eigensolvers, quantum approximate optimization, quantum machine learning, and other applications. PennyLane offers a unified architecture with a plugin system that supports qubit and continuous‑variable devices, enables gradient computation compatible with classical backpropagation, and integrates with quantum simulators, hardware providers such as Xanadu Cloud, Amazon Braket, IBM Quantum, and machine‑learning libraries like TensorFlow, PyTorch, JAX, and Autograd. PennyLane extends automatic differentiation algorithms from optimization and machine learning to quantum and hybrid computations.

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.