Blance, Andrew and Spannowsky, Michael (2021) 'Quantum machine learning for particle physics using a variational quantum classifier.', Journal of high energy physics., 2021 .
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.
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|Publisher Web site:||https://doi.org/10.1007/JHEP02(2021)212|
|Publisher 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.|
|Date accepted:||12 January 2021|
|Date deposited:||13 April 2021|
|Date of first online publication:||24 February 2021|
|Date first made open access:||13 April 2021|
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