基于自适应量子拓扑的机器学习算法

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel machine learning algorithm based on the principles of quantum topology. We propose a framework that leverages the unique properties of quantum topology to enhance data analysis and feature extraction, potentially surpassing the capabilities of traditional machine learning models. The core mechanism of this algorithm involves an adaptive parameter adjustment process driven by data distribution, enabling automated optimization and improved model performance. The proposed method offers a promising approach to addressing challenges associated with dynamic data and provides a foundation for future research in quantum-enhanced machine learning.

DOI: 10.5281/zenodo.22187651

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