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Lecture notes in computer science

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

Ravi Kumar Jha, Nikola Kirilov Kasabov, Damien Coyle, Saugat Bhattacharyya, Girijesh Prasad

Published Jan 1, 2025• 3 citations
Publisher
DOI
10.1007/978-981-96-6579-2_25

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Abstract Mental stress detection through electroencephalography (EEG) signals faces difficulties because EEG signals display non-stationary behavior and are vulnerable to artifact interference. The research introduces a hybrid deep-quantum learning framework which combines a one-dimensional convolutional neural network (1D-CNN) with a quantum support vector classifier (QSVC) to differentiate between stress and calm states. The CNN functions as a trainable feature extractor which converts raw EEG time-series data into compressed latent representations that undergo mapping to a quantum Hilbert space through ZZFeatureMap before being classified by a quantum kernel. The research demonstrates through experiments on a multi-subject EEG dataset that the hybrid model reaches its highest accuracy of (81.00%) (F1: 0.81) under optimal configuration using 8-dimensional features with MinMax scaling to [0,1] and 1000 training samples because it surpasses both the classical SVM (76.80%) and end-to-end CNN (78.85%) baselines. The analysis demonstrates that quantum kernel performance depends on feature dimensionality and scaling because lower dimensions (F-4) lack expressivity while higher dimensions (F-12) reach a performance limit and scaling to [-1,1] causes accuracy loss of 16 percentage points when compared to [0,1] scaling. The quantum classifier shows good sample efficiency because it gains more from additional training data compared to the traditional model. The research demonstrates that hybrid deep-quantum architectures function in the NISQ era while creating a performance standard for quantum-based physiological signal processing.
Quantum algorithms have become a popular research domain in recent times for discovering quantum-enhanced solutions in machine learning applications. Quantum kernels are one of the directions that establish such quantum-enhanced solutions to some extent. This work presents a detailed analysis of the quantum kernel approach leveraging feature maps and relevant hyperparameters to develop enhanced quantum kernels. The study includes a new high-order feature map and assesses five existing state-of-the-art feature maps for enhanced quantum kernel classifiers. Additionally, the significance of the rotational factor as a hyperparameter is highlighted for improving kernel performance. Also, it is analyzed whether different hyperparameter-tuned feature maps can lead to enhanced decision boundaries, demonstrating kernel expressivity. The analysis is undertaken on classification tasks using four different nonlinear datasets of distinct complexity. Comparative evaluations are also made with traditional machine learning models-Support Vector Machines (Linear and RBF), Naïve Bayes, Linear Discriminant Analysis, Decision Tree, Random Forest, Adaptive Boosting, and MLP. Overall, the study demonstrates that a well-tuned quantum feature map can enhance the generalization ability of quantum kernels, making them more effective for broader quantum-enhanced machine learning applications.