基于量子自适应拓扑的机器学习算法

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces a novel machine learning algorithm, termed Quantum Adaptive Topology-Based Learning (QATL), leveraging the principles of quantum self-adapted topology to enhance learning capabilities. We demonstrate the effectiveness of QATL through the successful application to a range of complex pattern recognition tasks. The algorithm's core mechanism involves dynamically adjusting a topological structure to capture intricate relationships within the data, leading to improved predictive accuracy and decision-making. The study explores the potential of this approach to overcome limitations of traditional machine learning techniques, particularly in situations involving high dimensionality and complex non-linear correlations. We analyze the algorithm's strengths and weaknesses, highlighting key design choices and potential avenues for future research. The paper concludes with a discussion of the implications of this approach for various applications, including anomaly detection and data compression.

DOI: 10.5281/zenodo.22249788

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