基于自适应量子网络的分布式优化

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel distributed optimization framework based on adaptive quantum networks, leveraging the inherent adaptability of quantum states to enhance the efficiency and accuracy of quantum machine learning models. Traditional distributed optimization algorithms often rely on static parameters, limiting the potential for automated optimization. We propose a framework that dynamically adjusts optimization parameters based on quantum state evolution, offering a more robust and flexible approach to algorithm improvement. The core mechanism centers around utilizing quantum state transformations to guide the optimization process, resulting in improved performance compared to conventional methods. This work contributes to the advancement of quantum machine learning by providing a practical and efficient solution for distributed optimization.

DOI: 10.5281/zenodo.22278755

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