Quantum-Assisted Generative AI for Simulation of the Calorimeter Response
As CERN approaches the launch of the High Luminosity Large Hadron Collider (HL-LHC) by the decade’s end, the computational demands of traditional simulations have become untenably high. Projections show millions of CPU-years required to create simulated datasets - with a substantial fraction of CPU time devoted to calorimetric simulations. This presents unique opportunities for breakthroughs in computational physics. We show how Quantumassisted Generative AI can be used for the purpose of creating synthetic, realistically scaled calorimetry dataset. The model is constructed by combining D-Wave’s Quantum Annealer processor with a Deep Learning architecture, increasing the timing performance with respect to first principles simulations and Deep Learning models alone, while maintaining current state-of-the-art data quality
Authors
- Eric Paquet (ORCID: https://orcid.org/0000-0001-6515-2556)
- M. Swiatlowski (ORCID: https://orcid.org/0000-0001-7287-0468)
- J. Quetzalcóatl Toledo-Marín (ORCID: https://orcid.org/0000-0001-6212-1033)
- Geoffrey Fox (ORCID: https://orcid.org/0000-0003-1017-1391)
- C. Gay (ORCID: https://orcid.org/0000-0002-9354-9507)
- Roger G. Melko (ORCID: https://orcid.org/0000-0002-5505-8176)
- W. Fedorko (ORCID: https://orcid.org/0000-0002-5138-3473)
- H. Jia (ORCID: https://orcid.org/0000-0002-7391-4423)
- I-Hsuan Lu
- Deniz Sogutlu
Institutions
- TRIUMF (CA)
- University of British Columbia (CA)
- Perimeter Institute (CA)
- University of Toronto (CA)
- National Research Council Canada (CA)
- University of Virginia (US)
Publication Details
- Journal
- EPJ Web of Conferences
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1051/epjconf/202533701233
- Primary Topic
- Particle physics theoretical and experimental studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- University of Virginia
- Government of Canada
- Mitacs
- Institut Périmètre de physique théorique