基于量子纠缠的自适应机器学习模型优化

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper explores the potential of utilizing quantum entanglement for adaptive machine learning model optimization. Traditional optimization algorithms often struggle with inherent limitations in convergence and stability, particularly when dealing with complex datasets and intricate model architectures. We propose a novel approach that leverages quantum entanglement to dynamically adjust model parameters, thereby enhancing model robustness and generalization ability. The core mechanism centers on establishing entangled states between model parameters and the optimization landscape, enabling instantaneous and coordinated adjustments. We analyze the advantages and challenges of this quantum-enhanced optimization paradigm, demonstrating its potential for significantly improving model performance across various machine learning tasks. This work contributes to the development of more stable and efficient optimization techniques, paving the way for advancements in deep learning and other areas of machine learning.

DOI: 10.5281/zenodo.22225181

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