异质性信息驱动的量子机器学习

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel approach to quantum machine learning leveraging the concept of inherent data distribution heterogeneity. We propose a quantum-enhanced algorithm designed to automatically adjust the parameters of machine learning models based on the distribution of data points. This method, termed "Heterogeneous Information Driven Quantum Machine Learning," aims to improve model generalization by intelligently adapting to the inherent variability within the data. The core mechanism involves utilizing quantum entanglement and superposition to effectively represent and analyze data distribution, allowing for a dynamic and informed parameter tuning process. We demonstrate the efficacy of this approach through simulations and preliminary experiments, highlighting its potential to overcome limitations of traditional machine learning techniques.

DOI: 10.5281/zenodo.22204690

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