Title: Topological Quantum Graph Neural Networks (TQNN)

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel type of Graph Neural Network (GNN) called Topological Quantum Graph Neural Networks (TQNN). TQNN leverages the inherent topological structure of graphs and the strengths of Graph Neural Networks (GNNs) to enhance model recognition and prediction capabilities. We propose a new architecture that utilizes a modified graph representation and a quantum-inspired transformation to improve the network's ability to capture complex relationships and patterns within data. The core mechanism involves incorporating a quantum-inspired transformation into the graph representation, which enables the network to represent and process information in a fundamentally different way, leading to significant improvements in performance across various tasks. This work presents a new approach to GNNs that addresses limitations of existing methods, offering a promising pathway for enhanced machine learning performance.

DOI: 10.5281/zenodo.22290665

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