Zephyr quantum-assisted hierarchical Calo4pQVAE for particle-calorimeter interactions

TL;DR

This paper introduces a quantum-assisted hierarchical deep generative model using a variational autoencoder and quantum annealer to simulate particle-calorimeter interactions more efficiently, addressing computational challenges in high-energy physics.

Contribution

It presents a novel hybrid quantum-classical deep generative framework leveraging a variational autoencoder and a quantum annealer-embedded RBM for particle shower simulation.

Findings

Significantly accelerated shower generation times using quantum simulation.

Effective integration of quantum annealer topology into deep generative models.

Potential to reduce computational costs of calorimeter simulations.

Abstract

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.