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

2025-05-30

AI Summary

To address the sparsity of new physics signals and the complexity of backgrounds in LHC proton–proton collision data, this work proposes a tensor-network-based latent-space anomaly detection method. First, an autoencoder learns a compact latent representation of high-dimensional collision events. Then, for the first time, a parameterized isometric matrix product state (MPS) is introduced to efficiently model the event distribution in this latent space and compute interpretable anomaly scores. Compared to state-of-the-art quantum-inspired approaches, our method achieves significantly improved detection sensitivity (+12.7% AUC) and computational efficiency (3.2× inference speedup) on simulated LHC data, while maintaining interpretability and scalability. This work constitutes the first empirical validation of isometric MPS for modeling latent distributions in high-energy physics, establishing a novel, theoretically rigorous, and practically viable tensor-network paradigm for new physics searches.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• Detecting anomalies in LHC proton collision events
• Using tensor networks for efficient anomaly detection
• Improving new-physics discovery with quantum-inspired methods

Innovation

• Tensor network for anomaly detection
• Matrix Product State with isometric map
• Latent space processing via autoencoder