INQA Seminar: Wojtek Fedorko, Triumf - September 24 2024

Title: Quantum Variational Autoencoder for Simulation of the Calorimetric Detectors at the Large Hadron Collider Experiments
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 Quantum Variational Autoencoder 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 hierarchical 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. Some details on new strategies for the mitigation of freezout effects and effective temperature scaling will be presented.