Quantum Graph Neural Network Adaptive Learning

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Graph Neural Network Adaptive Learning is a novel framework for training graph neural networks (GNNs) through an adaptive learning process. Traditional GNN models rely heavily on classical computer computations, limiting their ability to effectively handle complex datasets. This paper introduces a quantum-enhanced GNN architecture that leverages the unique properties of quantum graphs to enable self-adaptative learning, resulting in improved model performance across diverse datasets. The core mechanism involves the utilization of quantum phenomena to optimize the training process, thereby overcoming the limitations of classical approaches. This research demonstrates the efficacy of this approach through rigorous analysis and empirical validation, showcasing its potential to significantly enhance the capabilities of GNN models.

DOI: 10.5281/zenodo.22511154

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