基于量子拓扑的自适应机器学习

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel self-adaptive machine learning approach leveraging the unique properties of quantum topology. Traditional machine learning algorithms struggle to effectively adapt to complex environments, necessitating the exploration of alternative paradigms. We propose a method that transforms machine learning models into quantum operations, facilitated by quantum entanglement, to achieve enhanced model adaptability and generalization capabilities. This research explores the potential of quantum topology as a foundation for a fundamentally different approach to machine learning, addressing limitations inherent in classical methods. The core mechanism focuses on utilizing quantum entanglement to enable dynamic model adjustment and improve the model's robustness to variations in input data. This work provides a foundation for future research into quantum-enhanced machine learning, offering a promising pathway for addressing challenges in data-intensive applications.

DOI: 10.5281/zenodo.22638204

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