| Title: |
Towards Efficient Quantum Anomaly Detection: One-Class SVMs Using Variable Subsampling and Randomized Measurements |
| Authors: |
Kölle, Michael; Ahouzi, Afrae; Debus, Pascal; Müller, Robert; Schuman, Daniëlle; Linnhoff-Popien, Claudia |
| Publication Year: |
2024 |
| Collection: |
Publikationsdatenbank der Fraunhofer-Gesellschaft |
| Subject Terms: |
Anomaly Detection; OC-SVM; Quantum Machine Learning |
| Description: |
324 ; 335 ; 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. |
| Document Type: |
conference object |
| Language: |
English |
| Relation: |
International Conference on Agents and Artificial Intelligence 2024; #PLACEHOLDER_PARENT_METADATA_VALUE#; ICAART 2024, 16th International Conference on Agents and Artificial Intelligence. Proceedings. Vol.2; https://publica.fraunhofer.de/handle/publica/495924 |
| DOI: |
10.5220/0012381200003636 |
| Availability: |
https://publica.fraunhofer.de/handle/publica/495924; https://doi.org/10.5220/0012381200003636 |
| Rights: |
true |
| Accession Number: |
edsbas.FD691D35 |
| Database: |
BASE |