Quantum machine learning for particle physics using a variational quantum classifier

Blance, Andrew; Spannowsky, Michael

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
• Electronic ISSN: 1029-8479
• 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
• Public URL: https://durham-repository.worktribe.com/output/1249842
• Related Public URLs: https://arxiv.org/abs/2010.07335