An Enhanced Classification Framework Based on Quantum Resonance Dimensionality Reduction and Hybrid Quantum Ensemble Learning

With the rapid growth of data volume and complexity, traditional machine learning algorithms encounter significant challenges in handling large-scale and high-dimensional datasets. To address these limitations, this paper proposes an enhanced classification framework based on Quantum Resonance Dimensionality Reduction (QRDR) and Hybrid Quantum Ensemble Learning (HQEL). First, a Multi-Kernel Quantum Support Vector Machine (MKQSVM) is introduced, which integrates the advantages of multiple quantum kernel functions and employs Bayesian optimization to automatically determine the optimal kernel combination. This approach enhances the model’s adaptability and generalization ability on heterogeneous data. Second, the hybrid quantum ensemble learning model combines MKQSVM and Quantum Convolutional Neural Network (QCNN) through a stacking ensemble strategy with a classical meta-learner, thereby achieving improved prediction accuracy and stability. Moreover, the proposed QRDR algorithm is utilized to reduce the dimensionality of high-dimensional data while retaining critical features and minimizing redundancy, which effectively improves both training efficiency and predictive performance of the quantum models. Experimental results demonstrate that the proposed method outperforms traditional machine learning approaches on multiple benchmark datasets, particularly in classifying complex and high-dimensional data.