Abstract 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).
Abstract 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).
Quantum kernel methods have been proposed as a promising approach for leveraging near-term quantum computers for supervised learning, yet rigorous benchmarks against strong classical baselines remain scarce. We present a comprehensive empirical study of quantum kernel support vector machines (QSVMs) across nine binary classification datasets, four quantum feature maps, three classical kernels, and multiple noise models, totalling 970 experiments with strict nested cross-validation.
Our analysis spans four phases: (i) statistical significance testing, revealing that none of 29 pairwise quantum-classical comparisons reach significance at α=0.05; (ii) learning curve analysis over six training fractions, showing steeper quantum slopes on six of eight datasets that nonetheless fail to close the gap to the best classical baseline; (iii) hardware validation on IBM ibm_fez (Heron r2), demonstrating kernel fidelity r≥0.976 across six experiments; and (iv) seed sensitivity analysis confirming reproducibility (mean CV 1.4%).
A Kruskal-Wallis factorial analysis reveals that dataset choice dominates performance variance (ε2=0.73), while kernel type accounts for only 9%. Spectral analysis offers a mechanistic explanation: current quantum feature maps produce eigenspectra that are either too flat or too concentrated, missing the intermediate profile of the best classical kernel, the radial basis function (RBF). Quantum kernel training (QKT) via kernel-target alignment yields the single competitive result -- balanced accuracy 0.968 on breast cancer -- but with ~2,000x computational overhead.
Our findings provide actionable guidelines for quantum kernel research. The complete benchmark suite is publicly available to facilitate reproduction and extension.
Cyber-physical control systems are critical infrastructures designed around highly responsive feedback loops that are measured and manipulated by hundreds of sensors and controllers. Anomalous data, such as from cyber-attacks, greatly risk the safety of the infrastructure and human operators. With recent advances in the quantum computing paradigm, the application of quantum in anomaly detection can greatly improve identification of cyber-attacks in physical sensor data. In this paper, we explore the use of strong pre-processing methods and a quantum-hybrid Support Vector Machine (SVM) that takes advantage of fidelity in parameterized quantum circuits to efficiently and effectively flatten extremely high dimensional data. Our results show an F-1 Score of 0.86 and accuracy of 87% on the HAI CPS dataset using an 8-qubit, 16-feature quantum kernel, performing equally to existing work and 14% better than its classical counterpart.
Early and accurate anomaly detection in Intensive Care Unit (ICU) monitoring is vital for improving patient outcomes and reducing mortality. Traditional machine learning techniques often face challenges in handling the noisy, high-dimensional, and limited datasets characteristic of ICU physiological signals. In this study, we present an early investigation of a quantum-enhanced framework employing a classically simulated Quantum Support Vector Machine (QSVM) to improve the detection of critical anomalies in real-world ICU time-series data. Utilizing the publicly available PhysioNet 2012 Challenge dataset, we extract comprehensive statistical and temporal features from vital signs including heart rate, blood pressure, respiratory rate, and oxygen saturation. Our QSVM model leverages a quantum kernel mapped via Qiskit’s ZZFeatureMap and is rigorously benchmarked against classical machine learning classifiers such as Support Vector Machines with radial basis function kernels, Random Forest, and XGBoost. Experimental results demonstrate that the QSVM shows competitive and robust classification performance, particularly excelling in scenarios with limited and noisy data, with an AUC of 0.92, particularly excelling in scenarios with limited and noisy data. Furthermore, the quantum kernel’s ability to implicitly project data into a high-dimensional Hilbert space provides enhanced separability of complex physiological patterns. This work constitutes one of the first simulation-based applications of quantum machine learning to critical care anomaly detection, underscoring the promise of hybrid quantum-classical approaches for advancing real-time clinical decision support systems.
Quantum computing represents a promising frontier for machine learning, offering new tools to tackle complex problems by leveraging high-dimensional feature spaces and the unique properties of quantum systems. In this context, quantum kernels have emerged as a particularly compelling technique, capable of revealing structures in data that are difficult to detect using classical methods (Schuld and Killoran, 2019 [5]). However, their practical impact in real-world applications remains largely unexplored. This work proposes a hybrid quantum-classical architecture for anomaly detection in industrial cyber-physical systems, using the SWaT testbed as a case study. In complex environments like SWaT, sophisticated cyber-attacks can mimic normal system behavior, making them difficult to identify (Goh et al., 2016 [1]). Quantum kernels may help distinguish such anomalies by enabling more expressive data representations. Experimental results demonstrate the viability of hybrid quantum approaches, providing promising indications for future developments in quantum-supported industrial system security.
Reproducibility archive (code + manuscript) for the paper "Benchmarking Quantum Kernel Support Vector Machines Against Classical Baselines on Tabular Data: A Rigorous Empirical Study with Hardware Validation" (arXiv:2604.18837). The archive contains the full Python benchmark framework, experiment configurations, unit tests, and the compiled manuscript. Quantum kernel methods have been proposed as a promising approach for leveraging near-term quantum computers for supervised learning, yet rigorous benchmarks against strong classical baselines remain scarce. We present a comprehensive empirical study of quantum kernel support vector machines (QSVMs) across nine binary classification datasets, four quantum feature maps, three classical kernels, and multiple noise models, totalling 970 experiments with strict nested cross-validation. Our analysis spans four phases: (i) statistical significance testing; (ii) learning curve analysis; (iii) hardware validation on IBM ibm_fez (Heron r2); and (iv) seed sensitivity analysis. A Kruskal–Wallis factorial analysis reveals that dataset choice dominates performance variance (ε2=0.73), while kernel type accounts for only 9%. Spectral analysis offers a mechanistic explanation: current quantum feature maps produce eigenspectra that are either too flat or too concentrated, missing the intermediate profile of the best classical kernel (RBF). The complete benchmark suite is publicly available to facilitate reproduction and extension.
Quantum Support Vector Regression (QSVR) has emerged as one of the most promising quantum machine learning algorithms for nonlinear regression on Noisy Intermediate-Scale Quantum (NISQ) devices. However, despite numerous simulation-based studies, experimental evaluations on physical quantum hardware remain relatively scarce. This work presents a comparative analysis of three regression approaches: classical Support Vector Regression (SVR) with a Radial Basis Function (RBF) kernel, classical SVR utilizing precomputed quantum kernels, and the native QSVR implementation available in the qiskit-machine-learning framework. Experiments were conducted on three benchmark regression datasets using three execution environments: a noiseless Qiskit Aer simulator, the IQM Adonis hardware-aware simulator, and the physical 5-qubit IQM Spark superconducting quantum computer (Odra 5) located at Wrocław University of Science and Technology. Multiple quantum feature maps and circuit depths were investigated, while model hyperparameters were optimized using grid search. The obtained results demonstrate that QSVR can be successfully executed on contemporary superconducting quantum hardware and, for selected datasets, achieves prediction accuracy comparable to or exceeding that of a classical RBF-based SVR. The experiments further reveal the significant influence of quantum hardware noise, feature map selection, and circuit depth on regression quality. Moreover, the implementation based on precomputed quantum kernels generally exhibits greater robustness on physical hardware than the native QSVR implementation. The presented benchmark provides an experimental assessment of QSVR under realistic execution conditions and contributes to the ongoing evaluation of quantum machine learning algorithms on current NISQ-era quantum computers.