Lightning plugins¶
- Release
0.41.1
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.
Backends¶
PennyLane-Lightning high performance simulators include the following backends:
lightning.qubit: a fast state-vector simulator written in C++ with optional OpenMP additions and parallelized gate-level SIMD kernels.lightning.gpu: a state-vector simulator based on the NVIDIA cuQuantum SDK. It notably implements a distributed state-vector simulator based on MPI.lightning.kokkos: 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: a tensor-network simulator based on the NVIDIA cuQuantum SDK. The supported methods are Matrix Product State (MPS) and Exact Tensor Network (TN).
If you’re not sure what simulator to use, check out our PennyLane performance page.
Devices¶
The Lightning ecosystem provides the following devices:
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},
}