Quantum anomaly detection in the latent space of proton collision events at the LHC

Understand

The ongoing quest to discover new phenomena at the LHC necessitates the continuous development of algorithms and technologies.

• Established approaches like machine learning, along with emerging technologies such as quantum computing show promise in the enhancement of experimental capabilities.
• In this work, we propose a strategy for anomaly detection tasks at the LHC based on unsupervised quantum machine learning, and demonstrate its effectiveness in identifying new phenomena.
• The designed quantum models, an unsupervised kernel machine and two clustering algorithms, are trained to detect new-physics events using a latent representation of LHC data, generated by an autoencoder designed to accommodate current quantum hardware limitations on problem size.