Quantum Support Vector Regression for Robust Anomaly Detection
This work addresses semi-supervised anomaly detection in IT security, presenting the first systematic empirical evaluation of Quantum Support Vector Regression (QSVR) on real IBM quantum hardware. Leveraging quantum kernel methods, we conduct experiments across 11 benchmark datasets, incorporating realistic noise modeling—including depolarizing, phase-damping, and bit-flip channels—as well as adversarial example generation to characterize QSVR’s performance limits on NISQ devices. Key findings are: (1) QSVR exhibits robustness under four common noise types and outperforms ideal simulations on two datasets; (2) it is highly sensitive to amplitude-damping noise and calibration errors; (3) it suffers significant adversarial vulnerability, with quantum noise degrading—not enhancing—its adversarial robustness. These results establish the first empirical benchmark for quantum machine learning in security-critical applications and provide critical risk insights for practical deployment.