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 (Hrsg.): In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence, Bd. 2 Setúbal: SciTePress. S. 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.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Informatik > Künstliche Intelligenz und Maschinelles Lernen
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISBN: 978-989-758-680-4
• Ort: Setúbal
• Sprache: Englisch