Quantum Machine Learning: Adaptive Parameter Adjustment

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) has emerged as a promising field with the potential to revolutionize various machine learning tasks. This paper explores the application of quantum algorithms to enhance the optimization process for machine learning models. Specifically, we propose a novel quantum-enhanced parameter adjustment algorithm, termed "Adaptive Quantum Parameter Adjustment" (AQPA), designed to dynamically adjust model parameters based on the learning process. The core of AQPA leverages quantum entanglement and quantum algorithms to achieve a higher degree of optimization efficiency compared to traditional methods. We present a detailed mathematical framework for the algorithm, including the quantum state representations and the adjustment process. The paper discusses the potential benefits of this approach, including improved accuracy, faster convergence, and the ability to handle complex loss functions. We provide a preliminary analysis of the algorithm's performance on several benchmark datasets, demonstrating its effectiveness. The aim is to contribute to the advancement of QML and offer a practical solution for optimizing machine learning models.

DOI: 10.5281/zenodo.22222537

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