PIRSA:23100113

4-partite Quantum-Assisted VAE as a calorimeter surrogate

Toledo Marín, J. (2023). 4-partite Quantum-Assisted VAE as a calorimeter surrogate. Perimeter Institute. https://pirsa.org/23100113

Toledo Marín, Javier. 4-partite Quantum-Assisted VAE as a calorimeter surrogate. Perimeter Institute, Oct. 27, 2023, https://pirsa.org/23100113 

          @misc{ pirsa_PIRSA:23100113,
            doi = {10.48660/23100113},
            url = {https://pirsa.org/23100113},
            author = {Toledo Mar{\'\i}n, Javier},
            keywords = {Other},
            language = {en},
            title = {4-partite Quantum-Assisted VAE as a calorimeter surrogate},
            publisher = {Perimeter Institute},
            year = {2023},
            month = {oct},
            note = {PIRSA:23100113 see, \url{https://pirsa.org}}
          }

Javier Toledo Marín

TRIUMF

Talk number
PIRSA:23100113
Collection
Talk Type
Subject
Abstract

Numerical simulations of collision events within the ATLAS experiment 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. Simulating a single LHC event with Geant4 currently devours around 1000 CPU seconds, with calorimeter simulations imposing substantial computational demands. 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. We also provide preliminary insights into the requisite infrastructure for large-scale deployment.

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