Zephyr quantum-assisted hierarchical Calo4pQVAE for particle-calorimeter interactions
This addresses a critical bottleneck in high-energy physics simulations for researchers at facilities like CERN, though it appears incremental as it builds on existing VAE and RBM methods with quantum enhancement.
The paper tackles the unsustainable computational demands of traditional Monte Carlo simulations for particle calorimeter interactions at the HL-LHC by proposing a quantum-assisted hierarchical deep generative surrogate model, which accelerates shower generation times significantly through integration with a D-Wave quantum annealer.
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.