Lightning plugins

Release

0.39.0

The Lightning plugin ecosystem provides fast state-vector and tensor network 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 3.10 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). The supported method is Matrix Product State (MPS).

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 tensor network simulator with NVIDIA cuQuantum library support.

Authors

Lightning is the work of many contributors.

If you are using Lightning for research, please cite:

@misc{
    asadi2024,
    title={{Hybrid quantum programming with PennyLane Lightning on HPC platforms}},
    author={Ali Asadi and Amintor Dusko and Chae-Yeun Park and Vincent Michaud-Rioux and Isidor Schoch and Shuli Shu and Trevor Vincent and Lee James O'Riordan},
    year={2024},
    eprint={2403.02512},
    archivePrefix={arXiv},
    primaryClass={quant-ph},
    url={https://arxiv.org/abs/2403.02512},
}

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