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Semantic Scholar Open Access 2020 149 sitasi

Quantum machine learning in high energy physics

W. Guan G. Perdue Arthur Pesah M. Schuld K. Terashi +2 lainnya
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Abstrak

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.

Topik & Kata Kunci

Physics Computer Science

Penulis (7)

W

W. Guan

G

G. Perdue

A

Arthur Pesah

M

M. Schuld

K

K. Terashi

S

S. Vallecorsa

J

J. Vlimant

Format Sitasi

APA MLA BibTeX
Guan, W., Perdue, G., Pesah, A., Schuld, M., Terashi, K., Vallecorsa, S. et al. (2020). Quantum machine learning in high energy physics. https://doi.org/10.1088/2632-2153/abc17d

Akses Cepat

Lihat di Sumber doi.org/10.1088/2632-2153/abc17d
Informasi Jurnal
Tahun Terbit
2020
Bahasa
en
Total Sitasi
149×
Sumber Database
Semantic Scholar
DOI
10.1088/2632-2153/abc17d
Akses
Open Access ✓