基于量子机器学习的异常检测算法

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel anomaly detection algorithm based on quantum machine learning. The algorithm leverages the unique properties of quantum mechanics to enhance the detection process, offering potential advantages over classical methods. We propose a quantum-enhanced clustering technique to identify anomalous data points. The core mechanism involves a quantum algorithm designed to effectively capture complex patterns within datasets. This approach aims to improve the accuracy and efficiency of anomaly detection, specifically by capitalizing on quantum phenomena such as superposition and entanglement. The paper details the algorithm's architecture, its quantum components, and the potential benefits it offers. We present preliminary results demonstrating the effectiveness of this algorithm in detecting various anomaly types. The core claim emphasizes the potential of quantum machine learning to revolutionize anomaly detection.

DOI: 10.5281/zenodo.22183748

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