Andrew Tulloch Blance andrew.t.blance@durham.ac.uk
PGR Student Doctor of Philosophy
Unsupervised event classification with graphs on classical and photonic quantum computers
Blance, Andrew; Spannowsky, Michael
Authors
Professor Michael Spannowsky michael.spannowsky@durham.ac.uk
Director
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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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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