基于自适应的量子机器学习算法

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning algorithm, termed "Adaptive Quantum Learning," designed to dynamically adjust model parameters and learning rates based on the data being processed. Traditional quantum machine learning algorithms often suffer from limited adaptability, hindering their effectiveness in handling complex data. Our proposed algorithm leverages a self-adaptive mechanism that allows the model to continuously refine its parameters and learning process, resulting in enhanced performance and robustness. We explore the core mechanism of this adaptive approach, detailing its implementation and potential benefits across various quantum machine learning tasks. The paper presents a framework for evaluating the algorithm's effectiveness through illustrative examples and a discussion of future research directions.

DOI: 10.5281/zenodo.22280969

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