基于非线性自适应图的量子机器学习

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning framework based on non-linear adaptive graphs (NLAGs). Traditional quantum machine learning often relies on static, pre-defined graphs, limiting the adaptability of models to complex data dynamics. Our approach leverages NLAGs to dynamically adjust graph parameters based on input data, enabling a more flexible and efficient quantum learning process. We demonstrate the effectiveness of this framework through theoretical analysis and preliminary experimental results, showcasing improved performance compared to existing quantum learning algorithms. The core mechanism centers around the continuous parameter adjustment within the NLAG, allowing the model to adapt to changing input conditions. This adaptability is crucial for tackling problems requiring nuanced and responsive decision-making.

DOI: 10.5281/zenodo.22184372

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