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

TL;DR

This paper proposes methods to improve quantum anomaly detection efficiency using variable subsampling and randomized measurements, significantly reducing computation time while maintaining or surpassing classical kernel performance.

Contribution

It introduces two novel approaches for quantum kernel evaluation that achieve linear time complexity, enabling scalable quantum machine learning applications.

Findings

Training time reduced by up to 95%

Inference time reduced by up to 25%

Randomized measurements outperform classical RBF kernel in average precision

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.