Adaptive Quantum Graph Convolutional: A Novel Approach to Quantum Neural Networks

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum neural networks (QNNs) hold immense potential for advanced machine learning, offering advantages in computation and representation compared to classical neural networks. However, designing effective QNN architectures requires careful consideration of the network structure and the learning process. This paper introduces Adaptive Quantum Graph Convolutional (AQGC), a novel quantum graph convolution algorithm that dynamically adjusts the weights of convolutional layers based on input data changes. The core mechanism involves a feedback loop that continuously recalibrates the weights to maintain optimal performance across diverse datasets. We demonstrate the effectiveness of AQGC through extensive experiments on benchmark datasets, showcasing its ability to significantly improve model generalization.

DOI: 10.5281/zenodo.22264213

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