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. 2023;2023(2):220.
doi: 10.1007/JHEP02(2023)220. Epub 2023 Feb 22.

Quantum anomaly detection for collider physics

Sulaiman Alvi  1   2 , Christian W Bauer  2 , Benjamin Nachman  2   3
Affiliations

Quantum anomaly detection for collider physics

Sulaiman Alvi et al. J High Energy Phys. 2023.

Abstract

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.

Keywords: Multi-Higgs Models; New Light Particles.

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