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

Stenzel, Gerhard; Zielinski, Sebastian ; Kölle, Michael; Altmann, Philipp ; Nüßlein, Jonas und Gabor, Thomas (2025): Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting. ICAART 2025: 17th International Conference on Agents and Artificial Intelligence, Porto, Portugal, 23. - 25. Februar 2025. Rocha, Ana Paula; Steels, Luc und Herik, H. Jaap van den (Hrsg.): In: Proceedings of the 17th International Conference on Agents and Artificial Intelligence, ICAART 2025 - (Volume 1), Setúbal: SciTePress. S. 715-723

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.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISBN: 978-989-758-737-5
• Ort: Setúbal