Quantum anomaly detection for collider physics

Alvi, Sulaiman; Bauer, Christian W.; Nachman, Benjamin

doi:10.1007/jhep02(2023)220

Quantum anomaly detection for collider physics

Journal Article · Wed Feb 22 00:00:00 EST 2023 · Journal of High Energy Physics (Online)

DOI:https://doi.org/10.1007/jhep02(2023)220· OSTI ID:1970164

Alvi, Sulaiman ^[1]; Bauer, Christian W. ^[2]; Nachman, Benjamin ^[3]

University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)

We explore the use of Quantum Machine Learning (QML) for anomaly detection at the Large Hadron Collider (LHC). In particular, we explore a semi-supervised approach in the four-lepton final state where simulations are reliable enough for a direct background prediction. This is a representative task where classification needs to be performed using small training datasets - a regime that has been suggested for a quantum advantage. We find that Classical Machine Learning (CML) benchmarks outperform standard QML algorithms and are able to automatically identify the presence of anomalous events injected into otherwise background-only datasets.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

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

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1970164

Journal Information:: Journal of High Energy Physics (Online), Journal Name: Journal of High Energy Physics (Online) Journal Issue: 2 Vol. 2023; ISSN 1029-8479

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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