Speaker
Description
One potential roadblock towards the HL-LHC experiment, scheduled to begin in 2029, is the computational demand of traditional collision simulations. Projections suggest current methods will require millions of CPU-years annually, far exceeding existing computational capabilities. Replacing the event showers module in calorimeters with quantum-assisted deep learning surrogates can help bridge the gap. We propose a quantum-assisted deep generative model that combines a variational autoencoder (VAE) with a Restricted Boltzmann Machine (RBM) embedded in its latent space. The RBM in latent space provides further expresiveness to the model. We leverage D-Wave’s Zephyr Quantum Annealer as a quantum version of an RBM. Our framework sets a path towards utilizing large-scale quantum simulations as priors in deep generative models and for high energy physics, in particular, to generate high-quality synthetic data for the HL-LHC experiments.
| Track | Detector simulation & event generation |
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