Towards Efficient Quantum Anomaly Detection: One-Class SVMs using
Variable Subsampling and Randomized Measurements
- URL: http://arxiv.org/abs/2312.09174v1
- Date: Thu, 14 Dec 2023 17:42:18 GMT
- Title: Towards Efficient Quantum Anomaly Detection: One-Class SVMs using
Variable Subsampling and Randomized Measurements
- Authors: Michael K\"olle, Afrae Ahouzi, Pascal Debus, Robert M\"uller, Danielle
Schuman, Claudia Linnhoff-Popien
- Abstract summary: Quantum computing allows significant advancements in kernel calculation and model precision.
We present two distinct approaches: utilizing randomized measurements to evaluate the quantum kernel and implementing the variable subsampling ensemble method.
Experimental results demonstrate a substantial reduction in training and inference times by up to 95% and 25% respectively.
Although unstable, the average precision of randomized measurements discernibly surpasses that of the classical Radial Basis Function kernel.
- Score: 4.180897432770239
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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