基于自适应图神经网络的动态量子算法

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel dynamic quantum algorithm leveraging self-adaptive graph neural networks (S-GNNs). Traditional quantum algorithms often rely on static parameter settings, limiting their adaptability to complex input data. Our approach introduces a dynamically adjusting S-GNN that automatically adjusts the connection weights and neuron counts, enabling more efficient quantum computation. We demonstrate the algorithm's effectiveness through a series of benchmark tests, highlighting its ability to navigate intricate quantum circuits and achieve improved performance compared to existing methods. The core mechanism centers on a feedback loop that continuously refines the network based on the input, optimizing for a specific quantum circuit's properties. This contributes to a significant advancement in quantum algorithm design and optimization.

DOI: 10.5281/zenodo.22186629

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