Towards Efficient Quantum Anomaly Detection: One-Class SVMs Using Variable Subsampling and Randomized Measurements
Michael Kölle, Afrae Ahouzi, Pascal Debus, Robert Müller, Daniëlle Schuman and Claudia Linnhoff-Popien
Abstract: Quantum computing, with its potential to enhance various machine learning tasks, allows significant advancements in kernel calculation and model precision. Utilizing the one-class Support Vector Machine alongside a quantum kernel, known for its classically challenging representational capacity, notable improvements in average precision compared to classical counterparts were observed in previous studies. Conventional calculations of these kernels, however, present a quadratic time complexity concerning data size, posing challenges in practical applications. To mitigate this, we explore two distinct approaches: utilizing randomized measurements to evaluate the quantum kernel and implementing the variable subsampling ensemble method, both targeting linear time complexity. Experimental results demonstrate a substantial reduction in training and inference times by up to 95\% and 25\% respectively, employing these methods. Although unstable, the average precision of randomized measurements discernibly surpasses that of the classical Radial Basis Function kernel, suggesting a promising direction for further research in scalable, efficient quantum computing applications in machine learning.
Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, pp. 324-335 (2024)
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Michael Kölle, Afrae Ahouzi, Pascal Debus, Robert Müller, Daniëlle Schuman, and Claudia Linnhoff-Popien. “Towards Efficient Quantum Anomaly Detection: One-Class SVMs Using Variable Subsampling and Randomized Measurements.” Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART , pp. 324-335 , 2024. https://doi.org/10.5220/0012381200003636
