Quantum anomaly detection for collider physics
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.
- Publication:
-
Journal of High Energy Physics
- Pub Date:
- March 2023
- DOI:
- 10.1007/JHEP02(2023)220
- arXiv:
- arXiv:2206.08391
- Bibcode:
- 2023JHEP...02..220A
- Keywords:
-
- Multi-Higgs Models;
- New Light Particles;
- High Energy Physics - Phenomenology;
- High Energy Physics - Experiment;
- Physics - Data Analysis;
- Statistics and Probability;
- Quantum Physics
- E-Print:
- 18 pages, 6 figures v2: updated acknowledgment, fixed typos related to the output of VQC and QCL