Sensitivity Analysis of Variational Quantum Classifiers for Identifying Dummy Power Traces in Side-Channel Analysis

The application of quantum machine learning (QML) to security-relevant problems has attracted growing attention, yet its practical behavior in realistic workloads remains insufficiently characterized. This paper investigates the feasibility and limitations of variational quantum classifiers (VQCs) for identifying dummy power traces in side-channel analysis (SCA). A controlled benchmarking framework is developed to evaluate training stability, sensitivity to key design parameters, and resource–performance trade-offs under realistic constraints. To move beyond idealized simulation, hardware-relevant factors, including finite measurement budgets and device noise, are incorporated, and inference robustness under degraded operating conditions is assessed. The results show that VQCs can capture meaningful discriminative patterns in structured side-channel data, although robustness and performance depend strongly on encoding strategy, circuit depth, and measurement conditions. These findings provide an empirical assessment of the potential and limitations of QML for side-channel security and offer practical guidance for future research.