Quantum Machine Learning in High Energy Physics

Journal Article · Mon May 18 00:00:00 UTC 2020 · Mach.Learn.Sci.Tech.

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

Guan, Wen ^[1]; Perdue, Gabriel  ^[2]; Pesah, Arthur  ^[3]; Schuld, Maria  ^[4]; Terashi, Koji  ^[5]; Vallecorsa, Sofia  ^[6]; Vlimant, Jean-Roch  ^[7]

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1. Wisconsin U., Madison
2. Fermilab
3. Denmark, Tech. U.
4. KwaZulu Natal U.
5. Tokyo U., ICEPP
6. CERN
7. Caltech

Machine learning has been used in high energy physics 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 High Energy Physics. This paper reviews the first generation of ideas that use quantum machine learning on problems in high energy physics and provide an outlook on future applications.

• Research Organization: CERN; Caltech; Denmark, Tech. U.; Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); KwaZulu Natal U.; Tokyo U., ICEPP; Wisconsin U., Madison
• Sponsoring Organization: US Department of Energy
• Grant/Contract Number: AC02-07CH11359
• OSTI ID: 1881953
• Report Number(s): FERMILAB-PUB-20-184-QIS; oai:inspirehep.net:1796743; arXiv:2005.08582
• Journal Information: Mach.Learn.Sci.Tech., Journal Name: Mach.Learn.Sci.Tech. Vol. 2
• Country of Publication: United States
• Language: English