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Anomaly detection in high-energy physics using a quantum autoencoder

Ngairangbam, Vishal S.; Spannowsky, Michael; Takeuchi, Michihisa

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Vishal S. Ngairangbam

Michihisa Takeuchi


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 tt 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.


Ngairangbam, V. S., Spannowsky, M., & Takeuchi, M. (2022). Anomaly detection in high-energy physics using a quantum autoencoder. Physical Review D, 105(9), Article 095004.

Journal Article Type Article
Acceptance Date Apr 13, 2022
Online Publication Date May 6, 2022
Publication Date 2022
Deposit Date Jul 26, 2022
Publicly Available Date Jul 26, 2022
Journal Physical Review D
Print ISSN 2470-0010
Electronic ISSN 2470-0029
Publisher American Physical Society
Peer Reviewed Peer Reviewed
Volume 105
Issue 9
Article Number 095004


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Copyright Statement
Published by the American Physical Society under the terms of<br /> the Creative Commons Attribution 4.0 International license.<br /> Further distribution of this work must maintain attribution to<br /> the author(s) and the published article’s title, journal citation,<br /> and DOI. Funded by SCOAP3.

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