Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting

Gerhard Stenzel, Sebastian Zielinski, Michael Kölle, Philipp Altmann, Jonas Nüßlein, Thomas Gabor

2025

Abstract

To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit execution. Quantum gate matrix caching reduces the overhead of repeated applications of the Kronecker product when applying a gate matrix to the state vector by storing decomposed partial matrices for each gate. Circuit splitting divides the circuit into sub-circuits with fewer gates by constructing a dependency graph, enabling parallel or sequential execution on disjoint subsets of the state vector. These techniques are implemented using the PyTorch machine learning framework. We demonstrate the performance of our approach by comparing it to other PyTorch-compatible quantum state-vector simulators. Our implementation, named Qandle, is designed to seamlessly integrate with existing machine learning workflows, providing a user-friendly API and compatibility with the OpenQASM format. Qandle is an open-source project hosted on GitHub and PyPI.

Paper Citation

in Harvard Style

Stenzel G., Zielinski S., Kölle M., Altmann P., Nüßlein J. and Gabor T. (2025). Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: QAIO; ISBN 978-989-758-737-5, SciTePress, pages 715-723. DOI: 10.5220/0013343500003890

in Bibtex Style

@conference{qaio25,
author={Gerhard Stenzel and Sebastian Zielinski and Michael Kölle and Philipp Altmann and Jonas Nüßlein and Thomas Gabor},
title={Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: QAIO},
year={2025},
pages={715-723},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013343500003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: QAIO
TI - Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting
SN - 978-989-758-737-5
AU - Stenzel G.
AU - Zielinski S.
AU - Kölle M.
AU - Altmann P.
AU - Nüßlein J.
AU - Gabor T.
PY - 2025
SP - 715
EP - 723
DO - 10.5220/0013343500003890
PB - SciTePress