Quantum Support Vector Regression for Robust Anomaly Detection
This work addresses anomaly detection in IT security by exploring quantum machine learning, but it is incremental as it builds on prior QSVR research with new benchmarks and noise analysis.
The study tackled robust anomaly detection by benchmarking Quantum Support Vector Regression (QSVR) on IBM quantum hardware with eleven datasets, showing it outperformed noiseless simulation on two datasets and exhibited robustness to certain quantum noises but vulnerability to adversarial attacks.
Anomaly Detection (AD) is critical in data analysis, particularly within the domain of IT security. In recent years, Machine Learning (ML) algorithms have emerged as a powerful tool for AD in large-scale data. In this study, we explore the potential of quantum ML approaches, specifically quantum kernel methods, for the application to robust AD. We build upon previous work on Quantum Support Vector Regression (QSVR) for semisupervised AD by conducting a comprehensive benchmark on IBM quantum hardware using eleven datasets. Our results demonstrate that QSVR achieves strong classification performance and even outperforms the noiseless simulation on two of these datasets. Moreover, we investigate the influence of - in the NISQ-era inevitable - quantum noise on the performance of the QSVR. Our findings reveal that the model exhibits robustness to depolarizing, phase damping, phase flip, and bit flip noise, while amplitude damping and miscalibration noise prove to be more disruptive. Finally, we explore the domain of Quantum Adversarial Machine Learning and demonstrate that QSVR is highly vulnerable to adversarial attacks and that noise does not improve the adversarial robustness of the model.