Anomaly detection in high-energy physics using a quantum autoencoder

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

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 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.

Citation

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. https://doi.org/10.1103/physrevd.105.095004

• 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
• DOI: https://doi.org/10.1103/physrevd.105.095004