Quantum anomaly detection in the latent space of proton collision events at the LHC
This work addresses the problem of discovering new physics phenomena at the LHC for high-energy physics researchers, representing an incremental advancement by applying quantum methods to an existing bottleneck.
The authors tackled anomaly detection in proton collision events at the LHC by proposing unsupervised quantum machine learning models, demonstrating that a quantum kernel-based model significantly outperforms classical counterparts in a specific regime.
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. For kernel-based anomaly detection, we implement an instance of the model on a quantum computer, and we identify a regime where it significantly outperforms its classical counterparts. We show that the observed performance enhancement is related to the quantum resources utilised by the model.