ArticleJournal of high energy physics : JHEP2023
Quantum anomaly detection for collider physics.
Article in Journal of high energy physics : JHEP, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 43 citations in OpenAlex.
- Quantum computing universal thermalization dynamics in a (2 + 1)D Lattice Gauge Theory.Nature communications · 2025Article
- Article
- Suppressing nonperturbative gauge errors in the thermodynamic limit using local pseudogenerators.Communications physics · 2025Article
- Simulating two-dimensional lattice gauge theories on a qudit quantum computer.Nature physics · 2025Article
- Hilbert space fragmentation at the origin of disorder-free localization in the lattice Schwinger model.Communications physics · 2025Article
- Unsupervised beyond-standard-model event discovery at the LHC with a novel quantum autoencoder.Quantum machine intelligence · 2025Article
- Stirring the false vacuum via interacting quantized bubbles on a 5,564-qubit quantum annealer.Nature physics · 2025Article
- Quantum simulation of the bosonic Kitaev chain.Nature communications · 2024Article
- Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets.Frontiers in artificial intelligence · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We explore the use of Quantum Machine Learning (QML) for anomaly detection at the Large Hadron Collider (LHC). In particular, we explore a semi-supervised approach in the four-lepton final state where simulations are reliable enough for a direct background prediction. This is a representative task where classification needs to be performed using small training datasets - a regime that has been suggested for a quantum advantage. We find that Classical Machine Learning (CML) benchmarks outperform standard QML algorithms and are able to automatically identify the presence of anomalous events injected into otherwise background-only datasets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.