First steps towards quantum machine learning applied to the classification of event-related potentials

Grégoire Cattan; Alexandre Quemy; Anton Andreev

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arXiv · 2302.02648

First steps towards quantum machine learning applied to the classification of event-related potentials

Grégoire Cattan·Alexandre Quemy·Anton Andreev

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Abstract

Low information transfer rate is a major bottleneck for brain-computer interfaces based on non-invasive electroencephalography (EEG) for clinical applications. This led to the development of more robust and accurate classifiers. In this study, we investigate the performance of quantum-enhanced support vector classifier (QSVC). Training (predicting) balanced accuracy of QSVC was 83.17 (50.25) %. This result shows that the classifier was able to learn from EEG data, but that more research is required to obtain higher predicting accuracy. This could be achieved by a better configuration of the classifier, such as increasing the number of shots.

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Grégoire Cattan, Alexandre Quemy, Anton Andreev. 2023-02-06. First steps towards quantum machine learning applied to the classification of event-related potentials. https://arxiv.org/abs/2302.02648

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