Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC

TL;DR

This paper introduces a tensor network-based method for anomaly detection in LHC data, leveraging quantum-inspired models to improve the discovery of new phenomena in particle physics.

Contribution

It presents a novel tensor network approach using a Matrix Product State with an isometric feature map for analyzing latent LHC data representations.

Findings

Superior performance over quantum methods in anomaly detection

Effective processing of latent representations from autoencoders

Potential to accelerate new-physics discovery at the LHC

Abstract

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.