基于自适应的量子机器学习的自适应优化算法

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum machine learning (QML) has emerged as a promising field, offering the potential to accelerate and enhance machine learning models. However, training complex QML models often requires substantial computational resources and can be challenging to scale. This paper introduces a novel approach to QML, leveraging dynamic adaptation of quantum optimization algorithms. We propose a system that adjusts the parameters of the quantum optimization algorithm in real-time based on the characteristics of the input data, resulting in improved model performance. The core mechanism is the implementation of a dynamically adaptive quantum optimization algorithm that continuously refines the parameter space to achieve optimal convergence. The proposed method addresses the limitations of static optimization techniques by enabling a more flexible and responsive learning process, paving the way for more efficient and effective QML applications. We provide a comprehensive analysis of the algorithm's performance, demonstrating its effectiveness through extensive experimentation.

DOI: 10.5281/zenodo.22293737

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