Abstract

The objective of this research is to develop a Hybrid Quantum–Classical Convolutional Neural Network (QC-CNN) framework that integrates parameterized quantum circuits (PQCs) within classical CNN architectures to enhance image classification performance. The proposed model leverages quantum principles such as superposition and entanglement for high-dimensional feature representation, achieving superior accuracy and reduced computational complexity. Experiments on MNIST and CIFAR-10 datasets demonstrated improved classification accuracy of 98.7% and 82.5%, respectively, surpassing traditional CNNs while reducing training time by 33% and parameters by 45%. Statistical analysis confirmed the significance of these improvements. Visualizations using t-SNE revealed enhanced class separability, and noise perturbation tests validated the model’s robustness. The results highlight the hybrid QC-CNN’s potential for efficient and scalable quantum-enhanced deep learning applications. Extended evaluations, including quantum-layer-depth analysis, robustness testing, and t-SNE visualization, empirically support the hybrid QC-CNN's effectiveness across varied scenarios.

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