基于自适应的非线性图的量子算法

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel quantum algorithm based on adaptive non-linear graphs, aiming to enhance computational and simulation capabilities, particularly in the realm of complex spacetime relationships. Traditional quantum algorithms often rely on explicit computation, offering limited adaptability. This research leverages the inherent properties of non-linear graphs – specifically, their ability to represent and evolve data distributions – to automatically learn and model the data, facilitating more efficient computations and simulations. The algorithm incorporates a graph neural network (GNN) to provide a mechanism for model explanation and improve the algorithm's interpretability. The core claim is that this approach provides a significant advancement over existing quantum algorithms, offering greater flexibility, improved efficiency, and enhanced model explicability.

DOI: 10.5281/zenodo.22480790

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