自适应多尺度量子图论算法

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel adaptive quantum graph theory algorithm designed to enhance the efficiency of multi-scale quantum graph theory algorithms. Traditional graph theory methods often rely on static, pre-defined designs, limiting their adaptability. This algorithm leverages the inherent self-adaptive properties of quantum graph theory to dynamically adjust graph parameters, allowing for optimization and improved performance across a wider range of scales. The core mechanism involves iterative parameter adjustments based on a feedback loop, ensuring the algorithm continuously refines its response to the input data. We demonstrate the effectiveness of this approach through a series of simulations and analysis, showcasing significant improvements in algorithm speed and scalability compared to traditional methods.

DOI: 10.5281/zenodo.22329928

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