自适应拓扑参数调整的量子机器学习

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning framework, termed "Adaptive Topology Parameter Adjustment" (ATPA), that dynamically adjusts quantum model parameters through a self-adaptive topology structure. Traditional machine learning often relies on static models, limiting the ability to effectively leverage quantum systems. ATPA addresses this limitation by employing a mechanism that automatically adjusts parameters, fostering increased model performance and adaptability. We detail the framework's design, the core mechanism, and the resulting benefits through theoretical analysis and illustrative examples. This work demonstrates the potential for significantly enhanced machine learning capabilities through the integration of adaptive topology in quantum systems.

DOI: 10.5281/zenodo.22294786

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