Version 1
: Received: 18 September 2024 / Approved: 19 September 2024 / Online: 19 September 2024 (10:54:27 CEST)
How to cite:
Díaz, G. J.; Steffenel, L. A.; Barrios, C. J.; Couturier, J. F. How to Build a Software Quantum Simulator. Preprints2024, 2024091497. https://doi.org/10.20944/preprints202409.1497.v1
Díaz, G. J.; Steffenel, L. A.; Barrios, C. J.; Couturier, J. F. How to Build a Software Quantum Simulator. Preprints 2024, 2024091497. https://doi.org/10.20944/preprints202409.1497.v1
Díaz, G. J.; Steffenel, L. A.; Barrios, C. J.; Couturier, J. F. How to Build a Software Quantum Simulator. Preprints2024, 2024091497. https://doi.org/10.20944/preprints202409.1497.v1
APA Style
Díaz, G. J., Steffenel, L. A., Barrios, C. J., & Couturier, J. F. (2024). How to Build a Software Quantum Simulator. Preprints. https://doi.org/10.20944/preprints202409.1497.v1
Chicago/Turabian Style
Díaz, G. J., Carlos Jaime Barrios and Jean Francois Couturier. 2024 "How to Build a Software Quantum Simulator" Preprints. https://doi.org/10.20944/preprints202409.1497.v1
Abstract
Software quantum simulators are the most accessible tools for designing and testing quantum algorithms. This paper presents a comprehensive approach to building a software-based quantum simulator based on classical computing architectures. We explore fundamental quantum computing concepts, including state vector representations, quantum gates, and memory management techniques. The simulator prototype implements various memory optimization strategies, such as full-state representation, dynamic state pruning, and shared memory parallelization with OpenMP and distributed memory models using MPI. Additionally, data compression techniques, like ZFP, are explored to enhance simulation performance by reducing memory footprint. The results are validated through performance comparisons with leading open-source quantum simulators, such as Intel-QS, QuEST, and qsim. Our findings highlight the trade-offs between computational overhead and memory efficiency. This demonstrates that a hybrid approach using distributed memory and compression offers the best scalability for simulating large quantum systems. This work provides a foundation for developing efficient quantum simulators supporting
more complex quantum algorithms on classical hardware.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
