Comparing Emerging and Hybrid Quantum–Kolmogorov Architectures for Image Classification

The rapid evolution of Artificial Intelligence has enabled significant progress in image classification, with emerging approaches extending traditional deep learning paradigms. This article presents an extended version of a paper originally introduced at ICCSA 2025, providing a broader comparative analysis of classical, spline-based, and quantum machine learning architectures. The study evaluates Convolutional Neural Networks (CNNs), Kolmogorov–Arnold Networks (KANs), Convolutional KANs (CKANs), and Quantum Convolutional Neural Networks (QCNNs) on the Labeled Faces in the Wild dataset. In addition to these baselines, two novel architectures are introduced: a fully quantum Kolmogorov–Arnold model (F-QKAN) and a hybrid KAN–Quantum network (H-QKAN) that combines spline-based feature extraction with variational quantum classification. Rather than targeting state-of-the-art performance, the evaluation focuses on analyzing the behaviour of these architectures in terms of accuracy, computational efficiency, and interpretability under a unified experimental protocol. Results show that the fully quantum F-QKAN achieves a test accuracy above 80%. The hybrid H-QKAN obtains the best overall performance, exceeding 92% accuracy with rapid convergence and stable training dynamics. Classical CNNs models remain state-of-the-art in terms of predictive performance, whereas CKANs offer a favorable balance between accuracy and efficiency. QCNNs show potential in ideal noise-free settings but are significantly affected by realistic noise conditions, motivating further investigation into hybrid quantum–classical designs.