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| DOI | Trouver le DOI : https://doi.org/10.1038/s41534-025-01040-x |
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| Auteur | Rechercher : Toledo-Marín, J. Quetzalcóatl; Rechercher : Gonzalez, Sebastian; Rechercher : Jia, Hao; Rechercher : Lu, Ian; Rechercher : Sogutlu, Deniz; Rechercher : Abhishek, Abhishek; Rechercher : Gay, Colin; Rechercher : Paquet, Eric1Identifiant ORCID : https://orcid.org/0000-0001-6515-2556; Rechercher : Melko, Roger G.; Rechercher : Fox, Geoffrey C.; Rechercher : Swiatlowski, Maximilian; Rechercher : Fedorko, Wojciech |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
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| Bailleur de fonds | Rechercher : Mitacs; Rechercher : Perimeter Institute for Theoretical Physics; Rechercher : National Research Council Canada; Rechercher : Natural Sciences and Engineering Research Council of Canada; Rechercher : National Science Foundation; Rechercher : U.S. Department of Energy |
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| Format | Texte, Article |
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| Sujet | computer science; quantum physics; quantum simulation |
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| Résumé | 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. |
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| Date de publication | 2025-07-25 |
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| Maison d’édition | Springer Nature
University of South Wales |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 21bbdbbc-4692-472b-9d8c-048a4d431895 |
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| Enregistrement créé | 2025-10-16 |
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| Enregistrement modifié | 2025-11-03 |
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