基于量子拓扑的自适应机器学习算法

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel machine learning algorithm based on quantum topology, termed "Quantum Adaptive Machine Learning" (QAML). QAML leverages the unique properties of quantum topology to dynamically adjust machine learning model parameters, resulting in enhanced generalization and robustness. The algorithm constructs a quantum topology model that adapts to the problem's characteristics, optimizing model performance across diverse scenarios. We present a theoretical framework and preliminary experimental results demonstrating the effectiveness of QAML in a range of machine learning tasks, highlighting its potential to overcome limitations of traditional approaches.

DOI: 10.5281/zenodo.22291528

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