Tensor network for anomaly detection in the latent space of proton collision events at the LHC

Abstract The pursuit of discovering new phenomena at the Large Hadron Collider (LHC) requires constant innovation in algorithms and technologies. Tensor networks are mathematical models at the intersection of classical and quantum machine learning, which present a promising and efficient alternative for tackling these challenges. In this study, we propose a tensor network-based strategy for anomaly detection at the LHC and demonstrate its superior performance in identifying new phenomena compared to established quantum methods. Our model is a parameterized matrix product state with an isometric feature map, processing a latent representation of simulated LHC data generated by an autoencoder. Our results highlight the potential of tensor networks to enhance new-physics discovery.