Unsupervised event classification with graphs on classical and photonic quantum computers

Blance, Andrew; Spannowsky, Michael

Abstract

Photonic Quantum Computers provide several benefits over the discrete qubit-based paradigm of quantum computing. By using the power of continuous-variable computing we build an anomaly detection model to use on searches for New Physics. Our model uses Gaussian Boson Sampling, a #P-hard problem and thus not efficiently accessible to classical devices. This is used to create feature vectors from graph data, a natural format for representing data of high-energy collision events. A simple K-means clustering algorithm is used to provide a baseline method of classification. We then present a novel method of anomaly detection, combining the use of Gaussian Boson Sampling and a quantum extension to K-means known as Q-means. This is found to give equivalent results compared to the classical clustering version while also reducing the O complexity, with respect to the sample’s feature-vector length, from O(N) to O(log(N)).

Citation

Blance, A., & Spannowsky, M. (2021). Unsupervised event classification with graphs on classical and photonic quantum computers. Journal of High Energy Physics, 2021(8), https://doi.org/10.1007/jhep08%282021%29170

• Journal Article Type: Article
• Acceptance Date: Aug 9, 2021
• Online Publication Date: Aug 31, 2021
• Publication Date: 2021
• Deposit Date: Nov 9, 2021
• Publicly Available Date: Nov 9, 2021
• Journal: Journal of High Energy Physics
• Print ISSN: 1126-6708
• Electronic ISSN: 1029-8479
• Publisher: Scuola Internazionale Superiore di Studi Avanzati (SISSA)
• Peer Reviewed: Peer Reviewed
• Volume: 2021
• Issue: 8
• DOI: https://doi.org/10.1007/jhep08%282021%29170
• Public URL: https://durham-repository.worktribe.com/output/1223930