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

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. The ongoing quest in particle physics to discover fundamentally new phenomena necessitates the continuous development of algorithms and technologies. The authors propose a methodology based on quantum machine learning that can identify new phenomena in proton collision experiments, showing that it can outperform its classical counterparts when sufficient quantum computing resources are utilized.