基于动态量子拓扑的自适应机器学习模型训练

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel approach to machine learning training utilizing the principles of dynamic quantum topology. Traditional machine learning methods often rely on static parameter adjustments, while this work leverages the inherent dynamics of quantum topology to optimize model training efficiency. We propose a method that dynamically adjusts model parameters based on the evolution of the quantum topology, resulting in enhanced training speed and potentially improved model performance. The core mechanism centers around a feedback loop that continuously monitors and modifies the quantum topology, guiding the model towards optimal solutions. The proposed approach offers a fundamentally different paradigm for training machine learning models, potentially unlocking new possibilities in efficiency and performance.

DOI: 10.5281/zenodo.22465626

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