Anomaly detection in high-energy physics using a quantum autoencoder

Show simple item record Ngairangbam, Vishal S. Spannowsky, Michael Takeuchi, Michihisa
dc.coverage.spatial United States of America 2022-06-29T12:39:33Z 2022-06-29T12:39:33Z 2022-05
dc.identifier.citation Ngairangbam, Vishal S.; Spannowsky, Michael and Takeuchi, Michihisa, "Anomaly detection in high-energy physics using a quantum autoencoder", Physical Review D, DOI: 10.1103/PhysRevD.105.095004, vol. 105, no. 9, May 2022. en_US
dc.identifier.issn 2470-0010
dc.identifier.issn 2470-0029
dc.description.abstract The lack of evidence for new interactions and particles at the Large Hadron Collider (LHC) has motivated the high-energy physics community to explore model-agnostic data-analysis approaches to search for new physics. Autoencoders are unsupervised machine learning models based on artificial neural networks, capable of learning background distributions. We study quantum autoencoders based on variational quantum circuits for the problem of anomaly detection at the LHC. For a QCD t-t background and resonant heavy-Higgs signals, we find that a simple quantum autoencoder outperforms classical autoencoders for the same inputs and trains very efficiently. Moreover, this performance is reproducible on present quantum devices. This shows that quantum autoencoders are good candidates for analysing high-energy physics data in future LHC runs.
dc.description.statementofresponsibility by Vishal S. Ngairangbam, Michael Spannowsky and Michihisa Takeuchi
dc.format.extent vol. 105, no. 9
dc.language.iso en_US en_US
dc.publisher American Physical Society en_US
dc.subject Large Hadron Collider en_US
dc.subject Autoencoders en_US
dc.subject Machine learning models en_US
dc.subject QCD en_US
dc.subject Anomaly detection en_US
dc.subject Heavy-Higgs en_US
dc.title Anomaly detection in high-energy physics using a quantum autoencoder en_US
dc.type Article en_US
dc.relation.journal Physical Review D

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