Quantum anomaly detection for collider physics
Sulaiman Alvi, C. Bauer, Benjamin Nachman
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.
Topics & Concepts
PhysicsLarge Hadron ColliderParticle physicsAnomaly (physics)Physics beyond the Standard ModelAnomaly detectionLeptonColliderTask (project management)QuantumMachine learningArtificial intelligenceNuclear physicsQuantum mechanicsComputer scienceSystems engineeringElectronEngineeringParticle physics theoretical and experimental studiesQuantum Computing Algorithms and ArchitectureParticle Detector Development and Performance