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Quantum pattern recognition algorithms for charged particle tracking

High-energy physics is facing a daunting computing challenge with the large datasets expected from the upcoming High-Luminosity Large Hadron Collider in the next decade and even more so at future colliders. A key challenge in the reconstruction of events of simulated data and collision data is the p...

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Detalles Bibliográficos
Autor principal: Gray, H. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8685607/
https://www.ncbi.nlm.nih.gov/pubmed/34923843
http://dx.doi.org/10.1098/rsta.2021.0103
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author Gray, H. M.
author_facet Gray, H. M.
author_sort Gray, H. M.
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description High-energy physics is facing a daunting computing challenge with the large datasets expected from the upcoming High-Luminosity Large Hadron Collider in the next decade and even more so at future colliders. A key challenge in the reconstruction of events of simulated data and collision data is the pattern recognition algorithms used to determine the trajectories of charged particles. The field of quantum computing shows promise for transformative capabilities and is going through a cycle of rapid development and hence might provide a solution to this challenge. This article reviews current studies of quantum computers for charged particle pattern recognition in high-energy physics. This article is part of the theme issue ‘Quantum technologies in particle physics’.
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spelling pubmed-86856072022-02-02 Quantum pattern recognition algorithms for charged particle tracking Gray, H. M. Philos Trans A Math Phys Eng Sci Articles High-energy physics is facing a daunting computing challenge with the large datasets expected from the upcoming High-Luminosity Large Hadron Collider in the next decade and even more so at future colliders. A key challenge in the reconstruction of events of simulated data and collision data is the pattern recognition algorithms used to determine the trajectories of charged particles. The field of quantum computing shows promise for transformative capabilities and is going through a cycle of rapid development and hence might provide a solution to this challenge. This article reviews current studies of quantum computers for charged particle pattern recognition in high-energy physics. This article is part of the theme issue ‘Quantum technologies in particle physics’. The Royal Society 2022-02-07 2021-12-20 /pmc/articles/PMC8685607/ /pubmed/34923843 http://dx.doi.org/10.1098/rsta.2021.0103 Text en © 2021 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Articles
Gray, H. M.
Quantum pattern recognition algorithms for charged particle tracking
title Quantum pattern recognition algorithms for charged particle tracking
title_full Quantum pattern recognition algorithms for charged particle tracking
title_fullStr Quantum pattern recognition algorithms for charged particle tracking
title_full_unstemmed Quantum pattern recognition algorithms for charged particle tracking
title_short Quantum pattern recognition algorithms for charged particle tracking
title_sort quantum pattern recognition algorithms for charged particle tracking
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8685607/
https://www.ncbi.nlm.nih.gov/pubmed/34923843
http://dx.doi.org/10.1098/rsta.2021.0103
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