| Download | - View final version: Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions (PDF, 3.2 MiB)
- View supplementary information: Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions (PDF, 472 KiB)
|
|---|
| DOI | Resolve DOI: https://doi.org/10.1038/s41534-025-01040-x |
|---|
| Author | Search for: Toledo-Marín, J. Quetzalcóatl; Search for: Gonzalez, Sebastian; Search for: Jia, Hao; Search for: Lu, Ian; Search for: Sogutlu, Deniz; Search for: Abhishek, Abhishek; Search for: Gay, Colin; Search for: Paquet, Eric1ORCID identifier: https://orcid.org/0000-0001-6515-2556; Search for: Melko, Roger G.; Search for: Fox, Geoffrey C.; Search for: Swiatlowski, Maximilian; Search for: Fedorko, Wojciech |
|---|
| Affiliation | - National Research Council of Canada. Digital Technologies
|
|---|
| Funder | Search for: Mitacs; Search for: Perimeter Institute for Theoretical Physics; Search for: National Research Council Canada; Search for: Natural Sciences and Engineering Research Council of Canada; Search for: National Science Foundation; Search for: U.S. Department of Energy |
|---|
| Format | Text, Article |
|---|
| Subject | computer science; quantum physics; quantum simulation |
|---|
| Abstract | Particle collisions at accelerators like the Large Hadron Collider (LHC), recorded by experiments such as ATLAS and CMS, enable precise standard model measurements and searches for new phenomena. Simulating these collisions significantly influences experiment design and analysis but incurs immense computational costs, projected at millions of CPU-years annually during the high luminosity LHC (HL-LHC) phase. Currently, simulating a single event with Geant4 consumes around 1000 CPU seconds, with calorimeter simulations especially demanding. To address this, we propose a conditioned quantum-assisted generative model, integrating a conditioned variational autoencoder (VAE) and a conditioned restricted Boltzmann machine (RBM). Our RBM architecture is tailored for D-Wave’s Pegasus-structured advantage quantum annealer for sampling, leveraging the flux bias for conditioning. This approach combines classical RBMs as universal approximators for discrete distributions with quantum annealing’s speed and scalability. We also introduce an adaptive method for efficiently estimating effective inverse temperature, and validate our framework on Dataset 2 of CaloChallenge. |
|---|
| Publication date | 2025-07-25 |
|---|
| Publisher | Springer Nature
University of South Wales |
|---|
| Licence | |
|---|
| In | |
|---|
| Related data | |
|---|
| Language | English |
|---|
| Peer reviewed | Yes |
|---|
| Export citation | Export as RIS |
|---|
| Report a correction | Report a correction (opens in a new tab) |
|---|
| Record identifier | 21bbdbbc-4692-472b-9d8c-048a4d431895 |
|---|
| Record created | 2025-10-16 |
|---|
| Record modified | 2025-11-03 |
|---|
