Hybrid Classical–Quantum Kernel Learning for Scalable and Secure Link State Prediction in Software-Defined Networks

Software-Defined Networks (SDNs) with their centralized control system and enhanced programmability requires a sophisticated approach to predict link states in complex network topologies. While predictive performance of traditional machine learning (ML) models remains high on static data, they struggle under dynamic and imbalanced network behavior. Although Quantum Machine Learning (QML) offers a promising solution by leveraging quantum-enhanced kernels to discover non-linear patterns, current quantum hardware constraints limit its standalone applicability. This paper presents an extended hybrid classical-quantum learning framework for SDN link state prediction under both benign (stable) and attack (compromised) conditions, integrating classical preprocessing with quantum kernel embedding via the ZZFeatureMap. A tunable parameter α ϵ [0,1] enables dynamic interpolation between classical Radial Basis Function (RBF) and quantum kernels. On a stratified 5,000-sample subset of the InSDN dataset, which contains both normal and attack traffic flows, the hybrid Quantum Support Vector Machine (QSVM) achieves 85% accuracy, 0.92 ROC–AUC, and 0.73 Average Precision (AP). Scalability experiments on 20,000 samples confirm stable performance using Nyström-approximated kernels. Comparative evaluations with existing core classical, gradient boosting, and deep learning approaches highlight hybrid QSVM’s tunable expressivity, controlled computational scaling, and robustness to data distribution shifts, demonstrating its potential for future quantum-enabled SDN analytics.