Quantum machine learning in high energy physics

Guan, Wen; Perdue, Gabriel; Pesah, Arthur; Schuld, Maria; Terashi, Koji; Vallecorsa, Sofia; Vlimant, Jean-Roch

doi:10.1088/2632-2153/abc17d

Title: Quantum machine learning in high energy physics

Journal Article · 31 March 2021 · Machine Learning: Science and Technology

DOI: https://doi.org/10.1088/2632-2153/abc17d · OSTI ID:1881953

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Univ. of Wisconsin, Madison, WI (United States)
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Technical Univ. of Denmark, Lyngby (Denmark)
Univ. of KwaZulu-Natal, Durban (South Africa)
Univ. of Tokyo (Japan)
European Organization for Nuclear Research (CERN), Geneva (Switzerland)
California Institute of Technology (CalTech), Pasadena, CA (United States)

Machine learning has been used in high energy physics (HEP) for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable with a classical computer. With the advent of noisy intermediate-scale quantum computing devices, more quantum algorithms are being developed with the aim at exploiting the capacity of the hardware for machine learning applications. An interesting question is whether there are ways to apply quantum machine learning to HEP. This paper reviews the first generation of ideas that use quantum machine learning on problems in HEP and provide an outlook on future applications.

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Research Organization:: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), High Energy Physics (HEP)

Grant/Contract Number:: AC02-07CH11359; SC0020416; SC0019227; 0000240323

OSTI ID:: 1881953

Report Number(s):: FERMILAB-PUB-20-184-QIS; arXiv:2005.08582; oai:inspirehep.net:1796743; TRN: US2307891

Journal Information:: Machine Learning: Science and Technology, Vol. 2, Issue 1; ISSN 2632-2153

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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