基于量子自适应的机器学习算法

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper presents a novel machine learning algorithm leveraging quantum self-adaptation to enhance model performance. Traditional machine learning relies heavily on manually defined model parameters, limiting potential for optimization. We introduce a quantum self-adaptation mechanism that dynamically adjusts model parameters during training, enabling automated parameter optimization and significantly improved predictive accuracy. This approach addresses limitations of existing methods by providing a computationally efficient and inherently adaptive learning strategy. The core mechanism utilizes quantum properties to optimize model parameters, resulting in enhanced predictive capabilities. The results demonstrate a substantial improvement in prediction accuracy compared to state-of-the-art machine learning models.

DOI: 10.5281/zenodo.22298387

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