Quantum Adaptive Algorithm-Based Graph Neural Network (QAGAN)

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the application of Quantum Adaptive Algorithms (QAA) to Graph Neural Networks (GNNs). We propose a novel QAGAN architecture that leverages quantum self-optimization to enhance the training and inference process of GNNs. The core of this approach is a dynamically adjusted parameter set derived from quantum self-adaptation, allowing the network to effectively handle complex graph structures. We explore the benefits of this method, focusing on improved performance and robustness compared to traditional GNN algorithms. The proposed QAGAN offers a promising pathway towards more efficient and scalable GNN applications across diverse domains.

DOI: 10.5281/zenodo.22188032

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