Performance Analysis of Quantum-Enhanced Kernel Classifiers Based on Feature Maps: A Case Study on EEG-BCI Data

Quantum machine learning (QML) has gained significant attention recently for exploring the quantum computing applications. In this work, we delve into QML applications using the quantum support vector classifier (QSVC). QSVC uses quantum feature maps to create quantum-enhanced kernels. These kernels have the potential to provide a quantum advantage over classical machine learning (ML) algorithms in data classification. In this study, we introduced a novel quantum feature map and analyzed its role in developing a quantum-enhanced classifier, thereby expanding the applications of QML. Additionally, the study explored the importance of hyperparameter tuning for achieving quantum enhancement. To demonstrate proof of concept, we analyzed motor imagery electroencephalogram (EEG) datasets using the quantum kernel. We compared the results from our novel quantum feature map with outcomes from state-of-the-art feature maps. Additionally, we assessed the performance of quantum kernels in comparison with classical kernels and other ML classifiers. Finally, the study demonstrates the analytical advantages of the proposed quantum feature map over state-of-the-art feature maps and various classical classifiers through classification accuracy.