Hybrid Quantum Kolmogorov-Arnold Network Autoencoders for Anomaly Detection
Moe Hdaib, Sutharshan Rajasegarar, Lei Pan
A novel neural network architecture, KolmogorovArnold Networks (KAN), inspired by the compositional principles of the Kolmogorov-Arnold representation theorem, has recently emerged as an alternative to traditional multilayer perceptron (MLP) networks. In this work, we propose a Quantum-enhanced Kolmogorov-Arnold Network Autoencoder designed for anomaly detection in complex high-dimensional datasets. By integrating classical and quantum machine learning paradigms, our hybrid architecture combines the expressive capabilities of quantumaugmented nonlinear transformations with the robustness of classical networks. Our proposed approach employs parameterized quantum circuits to encode data into quantum states. These features are processed using quantum B-spline basis functions, enabling efficient function approximation while maintaining interpretability. We evaluate the performance of the proposed HQKAN Autoencoder on benchmark anomaly detection datasets, showcasing superior performance in terms of accuracy, generalization, and robustness against state-of-the-art classical baselines, which demonstrate promise for anomaly detection and complex data analysis.
International Conference on Quantum Communications, Networking, and Computing (QCNC)•Apr 6, 2026•0 citations