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Unsupervised event classification with graphs on classical and photonic quantum computers

Blance, Andrew; Spannowsky, Michael

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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

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.





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