基于量子自适应的机器学习的正则化策略

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel regularization strategy based on quantum self-adaptation, aiming to automate the selection and tuning of regularization parameters. Traditional regularization methods rely on manual adjustments, often leading to suboptimal model performance. This work leverages the principles of quantum mechanics to dynamically adjust the regularization strength, resulting in improved generalization capabilities. We present a framework where the quantum self-adaptation process optimizes the regularization parameter space directly, eliminating the need for manual parameter tuning. This approach offers a promising alternative to conventional regularization techniques, potentially leading to more robust and accurate models. The theoretical foundations and experimental validation demonstrate the effectiveness of this quantum self-adaptation strategy in various machine learning tasks.

DOI: 10.5281/zenodo.22203439

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