Quantum-inspired machine learning on high-energy physics data

Abstract Tensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems. Exploiting a tree tensor network, we apply a quantum-inspired machine learning technique to a very important and challenging big dat...

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Autores principales:	Timo Felser, Marco Trenti, Lorenzo Sestini, Alessio Gianelle, Davide Zuliani, Donatella Lucchesi, Simone Montangero
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/6d0b3f09c5cb4906b789bc66253206f4
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spelling	oai:doaj.org-article:6d0b3f09c5cb4906b789bc66253206f42021-12-02T16:09:44ZQuantum-inspired machine learning on high-energy physics data10.1038/s41534-021-00443-w2056-6387https://doaj.org/article/6d0b3f09c5cb4906b789bc66253206f42021-07-01T00:00:00Zhttps://doi.org/10.1038/s41534-021-00443-whttps://doaj.org/toc/2056-6387Abstract Tensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems. Exploiting a tree tensor network, we apply a quantum-inspired machine learning technique to a very important and challenging big data problem in high-energy physics: the analysis and classification of data produced by the Large Hadron Collider at CERN. In particular, we present how to effectively classify so-called b-jets, jets originating from b-quarks from proton–proton collisions in the LHCb experiment, and how to interpret the classification results. We exploit the Tensor Network approach to select important features and adapt the network geometry based on information acquired in the learning process. Finally, we show how to adapt the tree tensor network to achieve optimal precision or fast response in time without the need of repeating the learning process. These results pave the way to the implementation of high-frequency real-time applications, a key ingredient needed among others for current and future LHCb event classification able to trigger events at the tens of MHz scale.Timo FelserMarco TrentiLorenzo SestiniAlessio GianelleDavide ZulianiDonatella LucchesiSimone MontangeroNature PortfolioarticlePhysicsQC1-999Electronic computers. Computer scienceQA75.5-76.95ENnpj Quantum Information, Vol 7, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95 Timo Felser Marco Trenti Lorenzo Sestini Alessio Gianelle Davide Zuliani Donatella Lucchesi Simone Montangero Quantum-inspired machine learning on high-energy physics data
description	Abstract Tensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems. Exploiting a tree tensor network, we apply a quantum-inspired machine learning technique to a very important and challenging big data problem in high-energy physics: the analysis and classification of data produced by the Large Hadron Collider at CERN. In particular, we present how to effectively classify so-called b-jets, jets originating from b-quarks from proton–proton collisions in the LHCb experiment, and how to interpret the classification results. We exploit the Tensor Network approach to select important features and adapt the network geometry based on information acquired in the learning process. Finally, we show how to adapt the tree tensor network to achieve optimal precision or fast response in time without the need of repeating the learning process. These results pave the way to the implementation of high-frequency real-time applications, a key ingredient needed among others for current and future LHCb event classification able to trigger events at the tens of MHz scale.
format	article
author	Timo Felser Marco Trenti Lorenzo Sestini Alessio Gianelle Davide Zuliani Donatella Lucchesi Simone Montangero
author_facet	Timo Felser Marco Trenti Lorenzo Sestini Alessio Gianelle Davide Zuliani Donatella Lucchesi Simone Montangero
author_sort	Timo Felser
title	Quantum-inspired machine learning on high-energy physics data
title_short	Quantum-inspired machine learning on high-energy physics data
title_full	Quantum-inspired machine learning on high-energy physics data
title_fullStr	Quantum-inspired machine learning on high-energy physics data
title_full_unstemmed	Quantum-inspired machine learning on high-energy physics data
title_sort	quantum-inspired machine learning on high-energy physics data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6d0b3f09c5cb4906b789bc66253206f4
work_keys_str_mv	AT timofelser quantuminspiredmachinelearningonhighenergyphysicsdata AT marcotrenti quantuminspiredmachinelearningonhighenergyphysicsdata AT lorenzosestini quantuminspiredmachinelearningonhighenergyphysicsdata AT alessiogianelle quantuminspiredmachinelearningonhighenergyphysicsdata AT davidezuliani quantuminspiredmachinelearningonhighenergyphysicsdata AT donatellalucchesi quantuminspiredmachinelearningonhighenergyphysicsdata AT simonemontangero quantuminspiredmachinelearningonhighenergyphysicsdata
_version_	1718384415220957184

Quantum-inspired machine learning on high-energy physics data

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