Paper ID: 2412.04677 • Published Dec 6, 2024
Zephyr quantum-assisted hierarchical Calo4pQVAE for particle-calorimeter interactions
TL;DR
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With the approach of the High Luminosity Large Hadron Collider (HL-LHC) era
set to begin particle collisions by the end of this decade, it is evident that
the computational demands of traditional collision simulation methods are
becoming increasingly unsustainable. Existing approaches, which rely heavily on
first-principles Monte Carlo simulations for modeling event showers in
calorimeters, are projected to require millions of CPU-years annually -- far
exceeding current computational capacities. This bottleneck presents an
exciting opportunity for advancements in computational physics by integrating
deep generative models with quantum simulations. We propose a quantum-assisted
hierarchical deep generative surrogate founded on a variational autoencoder
(VAE) in combination with an energy conditioned restricted Boltzmann machine
(RBM) embedded in the model's latent space as a prior. By mapping the topology
of D-Wave's Zephyr quantum annealer (QA) into the nodes and couplings of a
4-partite RBM, we leverage quantum simulation to accelerate our shower
generation times significantly. To evaluate our framework, we use Dataset 2 of
the CaloChallenge 2022. Through the integration of classical computation and
quantum simulation, this hybrid framework paves way for utilizing large-scale
quantum simulations as priors in deep generative models.