Quadri-partite Quantum-Assisted VAE as a calorimeter surrogate*

Simulations of collision events at experiments like ATLAS and CMS have played a pivotal role in shaping the design of future experiments and analyzing ongoing ones. However, the quest for accuracy in describing Large Hadron Collider (LHC) collisions comes at an imposing computational cost, with projections estimating the need for millions of CPU-years annually during the High Luminosity LHC (HL-LHC) run [1]. Simulating a single LHC event with Geant4 currently devours around 1000 CPU seconds, with calorimeter simulations imposing substantial computational demands [2]. To address this challenge, we propose a Quantum-Assisted deep generative model. Our model marries a variational autoencoder (VAE) on the exterior with a Restricted Boltzmann Machine (RBM) in the latent space, delivering enhanced expressiveness compared to conventional VAEs. The RBM nodes and connections are meticulously engineered to enable the use of qubits and couplers on D-Wave's Pegasus Quantum Annealer for sampling and training. We also provide preliminary insights into the requisite infrastructure for large-scale deployment.

[1] A. Collaboration, ATLAS software and computing HL-LHC roadmap, Tech. Rep. (Technical report, CERN,Geneva. http://cds. cern. ch/record/2802918, 2022).

[2] D. Rousseau, Experimental particle physics and artificial intelligence, in Artificial Intelligence for Science: A Deep Learning Revolution (World Scientific, 2023) pp.447–464