mlsanityData to 14 Oct 2026

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

arXiv 2506.00102 ↗First posted 30 May 2025hep-ph cond-mat.stat-mech cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 19 of its 64 references that are in the map. First posted on arXiv 30 May 2025.

Builds on

19 of 64 references.

References · 19

  1. 2017Unsupervised Generative Modeling Using Matrix Product Statescited by 88
  2. 2018Variational Autoencoders for New Physics Mining at the Large Hadron Collidercited by 41
  3. 2020Variational Autoencoders for Anomalous Jet Taggingcited by 33
  4. 2023Tensor Networks Meet Neural Networks: A Survey and Future Perspectivescited by 32
  5. 2024CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networkscited by 32
  6. 2020Quantum-inspired Machine Learning on high-energy physics datacited by 31
  7. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 30
  8. 2023Quantum anomaly detection in the latent space of proton collision events at the LHCcited by 21
  9. 2021Bump Hunting in Latent Spacecited by 19
  10. 2023Tensor networks for interpretable and efficient quantum-inspired machine learningcited by 17
  11. 2023Tensor Networks for Explainable Machine Learning in Cybersecuritycited by 14
  12. 2021Dual-Parameterized Quantum Circuit GAN Model in High Energy Physicscited by 9
7 more
  1. 2021Quantum-inspired event reconstruction with Tensor Networks: Matrix Product Statescited by 9
  2. 2021Autoencoders for Semivisible Jet Detectioncited by 9
  3. 2023Efficient tensor network simulation of IBM's largest quantum processorscited by 9
  4. 2023Generative Learning of Continuous Data by Tensor Networkscited by 9
  5. 2019Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealercited by 7
  6. 2025tn4ml: Tensor Network Training and Customization for Machine Learningcited by 5
  7. 2024Ultra-low latency quantum-inspired machine learning predictors implemented on FPGAcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Hardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliderscited by 2
  2. 2026Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approachcited by 0
  3. 2026Quantum-inspired tensor networks in machine learning modelscited by 0
  4. 2026Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experimentscited by 0
  5. 2026Machine learning for the LHC physics program: a 2025-2026 stocktakecited by 0