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Quantum machine learning for particle physics using a variational quantum classifier

Blance, Andrew; Spannowsky, Michael

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Abstract

Quantum machine learning aims to release the prowess of quantum computing to improve machine learning methods. By combining quantum computing methods with classical neural network techniques we aim to foster an increase of performance in solving classification problems. Our algorithm is designed for existing and near-term quantum devices. We propose a novel hybrid variational quantum classifier that combines the quantum gradient descent method with steepest gradient descent to optimise the parameters of the network. By applying this algorithm to a resonance search in di-top final states, we find that this method has a better learning outcome than a classical neural network or a quantum machine learning method trained with a non-quantum optimisation method. The classifiers ability to be trained on small amounts of data indicates its benefits in data-driven classification problems.

Citation

Blance, A., & Spannowsky, M. (2021). Quantum machine learning for particle physics using a variational quantum classifier. Journal of High Energy Physics, 2021, Article 212. https://doi.org/10.1007/jhep02%282021%29212

Journal Article Type Article
Acceptance Date Jan 12, 2021
Online Publication Date Feb 24, 2021
Publication Date 2021
Deposit Date Apr 13, 2021
Publicly Available Date Apr 13, 2021
Journal Journal of High Energy Physics
Print ISSN 1126-6708
Publisher Scuola Internazionale Superiore di Studi Avanzati (SISSA)
Peer Reviewed Peer Reviewed
Volume 2021
Article Number 212
DOI https://doi.org/10.1007/jhep02%282021%29212
Related Public URLs https://arxiv.org/abs/2010.07335

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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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