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

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Pubblicato in:	arXiv.org (Dec 14, 2023), p. n/a
Autore principale:	Kölle, Michael
Altri autori:	Ahouzi, Afrae, Debus, Pascal, Müller, Robert, Schuman, Danielle, Linnhoff-Popien, Claudia
Pubblicazione:	Cornell University Library, arXiv.org
Soggetti:	Machine learning Quantum computing Radial basis function Complexity Anomalies Support vector machines Kernel functions Cognitive tasks
Accesso online:	Citation/Abstract Full text outside of ProQuest
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Descrizione
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.
ISSN:	2331-8422
Fonte:	Engineering Database