基于量子计算的自适应机器学习模型训练

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper investigates the potential of quantum computing to revolutionize machine learning, specifically focusing on the development of an adaptive machine learning model training algorithm. Traditional machine learning algorithms often struggle with high-dimensional datasets and complex non-linear relationships, limiting their scalability. Quantum computing offers the promise of significantly enhanced computational power through parallel processing, addressing these limitations. We propose a novel quantum-enhanced training algorithm that leverages quantum algorithms to accelerate the learning process, specifically targeting the improvement of model accuracy and generalization capabilities. The core mechanism centers around the application of quantum approximation algorithms to optimize the learning process, leading to faster convergence and improved performance on challenging datasets. The paper presents a conceptual framework, explores potential implementation challenges, and discusses future research directions.

DOI: 10.5281/zenodo.22263833

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