Quantum-Assisted Generative AI for Simulation of the Calorimeter Response

¹ TRIUMF, Vancouver, BC V6T 2A3, Canada
² Perimeter Institute for Theoretical Physics, Waterloo, ON, N2L 2Y5, Canada
³ University of Virginia, Charlottesville, VA, 22911, USA
⁴ University of British Columbia, Vancouver, BC V6T 1Z4, Canada
⁵ University of Toronto, Toronto, ON, M5S 1A1, Canada
⁶ National Research Council, Ottawa, ON, K1A 0R6, Canada

^* e-mail: wfedorko@triumf.ca

Published online: 7 October 2025

Abstract

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