mlsanityData to 14 Oct 2026

Quantum anomaly detection in the latent space of proton collision events at the LHC

arXiv 2301.10780 ↗First posted 25 Jan 2023quant-ph cs.LG hep-excited by 21

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Abstract (arXiv)

The ongoing quest to discover new phenomena at the LHC necessitates the continuous development of algorithms and technologies. Established approaches like machine learning, along with emerging technologies such as quantum computing show promise in the enhancement of experimental capabilities. In this work, we propose a strategy for anomaly detection tasks at the LHC based on unsupervised quantum machine learning, and demonstrate its effectiveness in identifying new phenomena. The designed quantum models, an unsupervised kernel machine and two clustering algorithms, are trained to detect new-physics events using a latent representation of LHC data, generated by an autoencoder designed to accommodate current quantum hardware limitations on problem size. For kernel-based anomaly detection, we implement an instance of the model on a quantum computer, and we identify a regime where it significantly outperforms its classical counterparts. We show that the observed performance enhancement is related to the quantum resources utilised by the model.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 24 of its 111 references that are in the map. First posted on arXiv 25 Jan 2023.

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24 of 111 references.

References · 24

  1. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 979
  2. 2018Supervised learning with quantum enhanced feature spacescited by 665
  3. 2020The power of quantum neural networkscited by 379
  4. 2020Power of data in quantum machine learningcited by 338
  5. 2016Quantum Machine Learningcited by 326
  6. 2020A rigorous and robust quantum speed-up in supervised machine learningcited by 271
  7. 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 233
  8. 2021Generalization in quantum machine learning from few training datacited by 216
  9. 2021Supervised quantum machine learning models are kernel methodscited by 200
  10. 2021Quantum advantage in learning from experimentscited by 188
  11. 2020Absence of Barren Plateaus in Quantum Convolutional Neural Networkscited by 125
  12. 2021Quantum machine learning beyond kernel methodscited by 92
12 more
  1. 2020Robust data encodings for quantum classifierscited by 91
  2. 2021The Inductive Bias of Quantum Kernelscited by 91
  3. 2022Exploiting symmetry in variational quantum machine learningcited by 69
  4. 2018Variational Autoencoders for New Physics Mining at the Large Hadron Collidercited by 41
  5. 2021Enhancing Generative Models via Quantum Correlationscited by 41
  6. 2021The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collidercited by 29
  7. 2021Automatic design of quantum feature mapscited by 28
  8. 2021Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstructioncited by 20
  9. 2021Bump Hunting in Latent Spacecited by 19
  10. 2020Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quarkcited by 18
  11. 2021Higgs analysis with quantum classifierscited by 14
  12. 2020Qibo: a framework for quantum simulation with hardware accelerationcited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 7; 2025: 7; 2026: 1.

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  1. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 30
  2. 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
  3. 2023Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernelcited by 6
  4. 2024Anomaly Detection for Real-World Cyber-Physical Security using Quantum Hybrid Support Vector Machinescited by 5
  5. 2025Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHCcited by 5
  6. 2023Generative Invertible Quantum Neural Networkscited by 3
  7. 2023Provable advantages of kernel-based quantum learners and quantum preprocessing based on Grover's algorithmcited by 3
  8. 2024Jet Discrimination with Quantum Complete Graph Neural Networkcited by 3
  9. 2024A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processorcited by 2
  10. 2026Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-trainingcited by 2
  11. 2023Higher-order topological kernels via quantum computationcited by 1
  12. 2024Geometric quantum machine learning of BQPA protocols and latent graph classifierscited by 1
9 more
  1. 2024Enhancing High-Energy Particle Physics Collision Analysis through Graph Data Attribution Techniquescited by 1
  2. 2024Automatic re-calibration of quantum devices by reinforcement learningcited by 0
  3. 2024Can Geometric Quantum Machine Learning Lead to Advantage in Barcode Classification?cited by 0
  4. 2025Quantum Hybrid Support Vector Machines for Stress Detection in Older Adultscited by 0
  5. 2025Guided Graph Compression for Quantum Graph Neural Networkscited by 0
  6. 2025Quantum-Hybrid Support Vector Machines for Anomaly Detection in Industrial Control Systemscited by 0
  7. 2025Emotion Recognition in Older Adults with Quantum Machine Learning and Wearable Sensorscited by 0
  8. 2025Fisher Information, Training and Bias in Fourier Regression Modelscited by 0
  9. 2025Quantum-Enhanced Generative Models for Rare Event Predictioncited by 0