  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.
