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

Michael Kölle, Afrae Ahouzi, Afrae Ahouzi, Pascal Debus, Robert Müller, Daniëlle Schuman, Claudia Linnhoff-Popien

2024

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.

Paper Citation

in Harvard Style

Kölle M., Ahouzi A., Debus P., Müller R., Schuman D. and Linnhoff-Popien C. (2024). Towards Efficient Quantum Anomaly Detection: One-Class SVMs Using Variable Subsampling and Randomized Measurements. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 324-335. DOI: 10.5220/0012381200003636

in Bibtex Style

@conference{icaart24,
author={Michael Kölle and Afrae Ahouzi and Pascal Debus and Robert Müller and Daniëlle Schuman and Claudia Linnhoff-Popien},
title={Towards Efficient Quantum Anomaly Detection: One-Class SVMs Using Variable Subsampling and Randomized Measurements},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={324-335},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012381200003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Towards Efficient Quantum Anomaly Detection: One-Class SVMs Using Variable Subsampling and Randomized Measurements
SN - 978-989-758-680-4
AU - Kölle M.
AU - Ahouzi A.
AU - Debus P.
AU - Müller R.
AU - Schuman D.
AU - Linnhoff-Popien C.
PY - 2024
SP - 324
EP - 335
DO - 10.5220/0012381200003636
PB - SciTePress