Lightning plugins

Release

0.37.0

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.

Devices

The Lightning ecosystem provides the following devices:

'lightning.qubit'

A fast state-vector qubit simulator written in C++

'lightning.gpu'

A heterogeneous backend state-vector simulator with NVIDIA cuQuantum library support.

'lightning.kokkos'

A heterogeneous backend state-vector simulator with Kokkos library support.

'lightning.tensor'

A heterogeneous backend tensor network simulator with NVIDIA cuQuantum library support.

index

 

Download Python script

 

Download Notebook

 

View on GitHub