Towards Efficient Quantum Anomaly Detection: One-Class SVMs Using Variable Subsampling and Randomized Measurements

Kölle, Michael; Ahouzi, Afrae; Debus, Pascal; Müller, Robert ; Schuman, Daniëlle und Linnhoff-Popien, Claudia (2024): Towards Efficient Quantum Anomaly Detection: One-Class SVMs Using Variable Subsampling and Randomized Measurements. ICAART 2024 : International Conference on Agents and Artificial Intelligence, Rome, Italy, 25.- 25. Februar 2024. Rocha, Ana Paula; Steels, Luc und Herik, Jaap van den (eds.) : In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence, Vol. 2 Setúbal: SciTePress. pp. 324-335

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.

• Item Type: Conference or Workshop Item (Paper)
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• ISBN: 978-989-758-680-4
• Place of Publication: Setúbal
• Language: English