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

• URL: http://arxiv.org/abs/2302.02648v1
• Date: Mon, 6 Feb 2023 09:43:25 GMT
• Title: First steps towards quantum machine learning applied to the classification of event-related potentials
• Authors: Gr\'egoire Cattan, Alexandre Quemy (PUT), Anton Andreev (GIPSA-Services)
• Abstract summary: Low information transfer rate is a major bottleneck for brain-computer interfaces based on non-invasive electroencephalography (EEG) for clinical applications. 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) %.
• Score: 68.8204255655161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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