Quantum Machine Learning: Dynamic Parameter Adjustment Algorithm

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel dynamic parameter adjustment algorithm for quantum machine learning models, designed to enhance model generalization and learning efficiency through adaptive parameter optimization guided by training data features. Traditional quantum machine learning approaches often rely on static parameter settings, limiting the model's capacity to fully exploit the intricacies of the data. Our proposed algorithm dynamically adjusts model parameters in response to training data characteristics, leading to improved performance across a range of tasks. We introduce the concept of "dynamic parameter adjustment" to facilitate this adaptive optimization process. This work offers a promising approach to overcome the limitations of static parameter tuning in quantum machine learning, potentially unlocking new capabilities in complex datasets.

DOI: 10.5281/zenodo.22205437

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