Hardware-Agnostic Quantum Kernel Feature Mapping for Anomaly Detection in Critical Infrastructure: A Cross-Testbed Validation on NISQ Processors

Modern Industrial Control Systems (ICS) face sophisticated cyber-physical attacks that exploit nonlinear correlations between process variables, rendering traditional linear classifiers ineffective. This paper presents a hardware-agnostic Quantum Support Vector Machine (QSVM) framework employing an 8-qubit ZZFeatureMap kernel for anomaly detection in critical water treatment and thermal power infrastructure. Performance benchmarks were established via noise-free statevector simulation to determine theoretical quantum advantage. Physical realizability was separately validated via circuit execution on IBM's 156-qubit ibm_fez processor, with completed hardware jobs enabling quantitative analysis of the simulation-to-hardware performance gap. Through rigorous cross-testbed validation on the SWaT (Secure Water Treatment) and HAI (Hardware-in-the-Loop Augmented ICS) datasets, our simulated approach achieves an AUC-ROC of 0.9912 ± 0.004 on SWaT and 0.8309 ± 0.050 on HAI, demonstrating consistent quantum advantage of +10.8% AUC over classical RBF-kernel SVMs on the more challenging HAI testbed. Statistical robustness is established through 5-seed cross-validation with stratified sampling. Hardware execution on ibm_fez confirms physical realizability with circuit depth 76 and 28 CNOT gates, while revealing an expected fidelity degradation of approximately 17–20% compared to ideal simulation due to gate errors and decoherence. The quantum kernel's Z _i Z _j entanglement structure geometrically captures pairwise feature correlations, providing inductive bias well-suited for coupled sensor dynamics. Unlike deep learning approaches that require massive attack datasets unavailable in critical infrastructure domains, our kernel method generalizes effectively from limited training samples. All experimental artifacts are publicly available at https://github.com/Ali-Badami/Quantum-IDS. IBM Quantum job identifiers: d5l9htjh36vs73bgsi3g (SWaT), d5l9huk8d8hc73cfb0pg (HAI).