Quantum machine learning in high energy physics

被引：82

作者：

Guan, Wen ^{[1
]}

Perdue, Gabriel ^{[2
]}

Pesah, Arthur ^{[3
]}

Schuld, Maria ^{[4
]}

Terashi, Koji ^{[5
]}

Vallecorsa, Sofia ^{[6
]}

Vlimant, Jean-Roch ^{[7
]}

机构：

[1] Univ Wisconsin, Madison, WI 53706 USA

[2] Fermilab Quantum Inst, Fermi Natl Accelerator Lab, POB 500, Batavia, IL 60510 USA

[3] Tech Univ Denmark, DTU Compute, Lyngby, Denmark

[4] Univ KwaZulu Natal, Sch Chem & Phys, ZA-4000 Durban, South Africa

[5] Univ Tokyo, ICEPP, Bunkyo Ku, 7-3-1 Hongo, Tokyo, JP 3001153, Japan

[6] CERN IT, 1 Esplanade Particules, CH-1211 Geneva, Switzerland

[7] CALTECH, PMA, Pasadena, CA 91125 USA

来源：

MACHINE LEARNING-SCIENCE AND TECHNOLOGY | 2021年 / 2卷 / 01期

关键词：

particle physics; quantum machine learning; quantum annealing; quantum circuit; quantum variational circuit; OPTIMIZATION; ALGORITHMS;

D O I：

10.1088/2632-2153/abc17d

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

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