Dynamic Adjustment Learning for Quantum Machine Learning

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers the potential to significantly accelerate and enhance machine learning tasks compared to classical algorithms. This paper introduces a novel Dynamic Adjustment Learning (DAL) algorithm specifically tailored for QML, designed to dynamically adjust model parameters based on training data. The core mechanism leverages the inherent properties of quantum mechanics – specifically superposition and entanglement – to achieve this adaptive learning process. We present a detailed theoretical analysis and empirical results demonstrating the effectiveness of DAL in improving model accuracy and generalization performance across various benchmark datasets. The paper concludes with a discussion of the limitations and future directions of this approach, highlighting its potential to revolutionize the field of QML.

DOI: 10.5281/zenodo.22595894

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