19–25 Oct 2024
Europe/Zurich timezone

Quantum-Assisted Generative AI for Simulation of the Calorimeter Response

22 Oct 2024, 10:00
30m
Large Hall

Large Hall

Talk Plenary Plenary session

Speaker

Wojtek Fedorko (TRIUMF)

Description

As CERN approaches the launch of the High Luminosity-LHC 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 Quantum-assisted 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.

Primary authors

Alison Lister (University of British Columbia (CA)) Colin Warren Gay (University of British Columbia (CA)) Mr Deniz Sogutlu (TRIUMF, UBC) Dr Eric Paquet (National Research Council Canada, University of Ottawa) Geoffrey Fox (University of Virginia) Hao Jia (University of British Columbia (CA)) Mr Ian Lu (TRIUMF / University of Toronto) J. Quetzalcoatl Toledo-Marin (TRIUMF) Maximilian J Swiatlowski (TRIUMF (CA)) Roger Melko Wojtek Fedorko (TRIUMF)

Presentation materials