Hybrid Quantum–classical Models For Enhanced Machine Learning Classification Using Ensemble Qsvm and Quantum Kernels

Quantum machine learning leverages properties such as superposition, interference, and entanglement to further enhance learning capability. However, hybrid classical–quantum approaches for complex data classification remain underexplored. To address this gap, this study proposes two novel hybrid techniques that integrate the strengths of classical and quantum models to improve classification performance. The first approach employs an ensemble framework that combines Quantum Support Vector Machine (QSVM), Multi-Layer Perceptron (MLP), and Random Forest using a majority voting strategy. The second approach utilizes quantum feature maps to project classical data into a high-dimensional quantum feature space via quantum kernels, followed by classification using a classical SVM. Both approaches undergo extensive hyperparameter tuning and are evaluated on benchmark datasets against existing methods. Experimental results demonstrate improved accuracy, robustness, and effectiveness of the proposed hybrid models, indicating their suitability for high-precision applications such as autonomous systems, fraud detection, and medical diagnostics.