Bridging Classical and Quantum Models via Attention-Guided Feature Distillation

While Quantum Neural Networks (QNNs) offer the potential for resource-efficient image classification, practical implementations are hindered by qubit instability and finite availability, causing them to often underperform classical counterparts. This performance disparity constitutes a critical research gap. We propose a novel hybrid classical-quantum knowledge distillation approach to bridge this gap. Our method utilizes a pre-trained classical CNN (AlexNet) as a teacher to guide the training of a quantum student model by minimizing the Kullback-Leibler (KL) divergence (D<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KL</inf>) between their output distributions. The quantum student model employs an architecture designed to facilitate intricate feature interactions using iterated CNOT gates. This technique substantially improves quantum model accuracy, demonstrating gains of 0.34% (6-class, 3 qubits) and 0.28% (8-class, 3 qubits) on the MNIST dataset, and 0.74% (8-class, 4 qubits). Our contribution lies in operationalizing a knowledge distillation paradigm for applied Quantum Machine Learning (QML), enabling the effective transfer of robust knowledge from established classical architectures to resource-constrained quantum models, thereby enhancing faster convergence and accelerating practical QML deployment.