Towards Efficient Quantum Anomaly Detection: One-Class SVMs using Variable Subsampling and Randomized Measurements
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| Publicat a: | arXiv.org (Dec 14, 2023), p. n/a |
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| Autor principal: | |
| Altres autors: | , , , , |
| Publicat: |
Cornell University Library, arXiv.org
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| Accés en línia: | Citation/Abstract Full text outside of ProQuest |
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| 022 | |a 2331-8422 | ||
| 035 | |a 2902167864 | ||
| 045 | 0 | |b d20231214 | |
| 100 | 1 | |a Kölle, Michael | |
| 245 | 1 | |a Towards Efficient Quantum Anomaly Detection: One-Class SVMs using Variable Subsampling and Randomized Measurements | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 14, 2023 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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. | |
| 653 | |a Machine learning | ||
| 653 | |a Quantum computing | ||
| 653 | |a Radial basis function | ||
| 653 | |a Complexity | ||
| 653 | |a Anomalies | ||
| 653 | |a Support vector machines | ||
| 653 | |a Kernel functions | ||
| 653 | |a Cognitive tasks | ||
| 700 | 1 | |a Ahouzi, Afrae | |
| 700 | 1 | |a Debus, Pascal | |
| 700 | 1 | |a Müller, Robert | |
| 700 | 1 | |a Schuman, Danielle | |
| 700 | 1 | |a Linnhoff-Popien, Claudia | |
| 773 | 0 | |t arXiv.org |g (Dec 14, 2023), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2902167864/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2312.09174 |