Lightning plugins

Release

0.37.0-dev46

_images/pennylane_lightning.png

The Lightning plugin ecosystem provides fast state-vector simulators written in C++.

PennyLane is a cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations. PennyLane supports Python 3.9 and above.

Features

PennyLane-Lightning high performance simulators include the following backends:

  • lightning.qubit: is a fast state-vector simulator written in C++.

  • lightning.gpu: is a state-vector simulator based on the NVIDIA cuQuantum SDK. It notably implements a distributed state-vector simulator based on MPI.

  • lightning.kokkos: is a state-vector simulator written with Kokkos. It can exploit the inherent parallelism of modern processing units supporting the OpenMP, CUDA or HIP programming models.

  • lightning.tensor: is a tensor network simulator based on the NVIDIA cuQuantum SDK (requires NVIDIA GPUs with SM 7.0 or greater). This device is designed to simulate large-scale quantum circuits using tensor networks. For small circuits, state-vector simulator plugins may be more suitable. The supported method is Matrix Product State (MPS). This device does not currently support finite shots. Currently, the supported measurement types are expectation values and the supported differentiation methods are parameter-shift and finite-diff. Note that only 1,2-wire gates and gates that can be decomposed by PennyLane into 1,2-wire gates are supported.