PennyLane: Automatic differentiation of hybrid quantum-classical computations
This provides a unified tool for researchers and developers working on near-term quantum computing applications, though it is incremental as it extends existing classical automatic differentiation techniques to quantum contexts.
PennyLane is a software framework that tackles the problem of differentiable programming for hybrid quantum-classical computations by enabling automatic differentiation of variational quantum circuits, integrating with classical machine learning libraries and quantum hardware providers.
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.