Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
This work addresses the problem of discovering new physics at the LHC for particle physicists, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled anomaly detection in proton collision events at the LHC by proposing a tensor network-based strategy, achieving superior performance compared to established quantum methods in identifying new phenomena.
The pursuit of discovering new phenomena at the Large Hadron Collider (LHC) demands constant innovation in algorithms and technologies. Tensor networks are mathematical models on the intersection of classical and quantum machine learning, which present a promising and efficient alternative for tackling these challenges. In this work, 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 parametrized 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.