自适应非线性模型参数调整的基于量子计算的算法

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper investigates a novel algorithm for optimizing machine learning models, specifically focusing on complex models, through the application of quantum computing. The core challenge lies in efficiently adapting model parameters to achieve optimal performance, which is computationally demanding for large datasets. We propose a method leveraging quantum parallelism to dynamically adjust model parameters, potentially accelerating the training process and enhancing model accuracy. The algorithm's underlying mechanism centers on utilizing quantum superposition and entanglement to explore a larger parameter space while minimizing computational cost. This research explores the potential of quantum mechanics to enhance machine learning workflows, specifically in parameter optimization.

DOI: 10.5281/zenodo.22632726

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