异质性信息驱动的量子机器学习模型

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning model, termed "Heterogeneity-Driven Quantum Machine Learning," that leverages the principle of data distribution heterogeneity to dynamically adjust model parameters and enhance generalization. Traditional machine learning relies heavily on manually tuned parameters, limiting its ability to effectively exploit complex data distributions. This work proposes a quantum-enhanced approach that utilizes the inherent variability within datasets to automatically optimize model parameters, resulting in improved performance across diverse datasets. The core mechanism involves quantum-assisted parameter adjustment guided by data heterogeneity, leading to a more robust and adaptable learning process. The model's effectiveness is demonstrated through a series of benchmark experiments, showcasing enhanced generalization capabilities compared to traditional quantum machine learning methods.

DOI: 10.5281/zenodo.22204739

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