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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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The Royal Society
2022
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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. |
collection | PubMed |
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’. |
format | Online Article Text |
id | pubmed-8685607 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT grayhm quantumpatternrecognitionalgorithmsforchargedparticletracking |