IEEE Transactions on Neural Networks and Learning Systems

A Quantum Spatial Graph Convolutional Neural Network Model on Quantum Circuits

Jin Yan Zheng, Qing Zhong Gao, MACIEJ J. OGORZAŁEK, Jinhu Lü, Yue Cheng Deng

Published Apr 15, 2024• 33 citations

Abstract

This article proposes a quantum spatial graph convolutional neural network (QSGCN) model that is implementable on quantum circuits, providing a novel avenue to processing non-Euclidean type data based on the state-of-the-art parameterized quantum circuit (PQC) computing platforms. Four basic blocks are constructed to formulate the whole QSGCN model, including the quantum encoding, the quantum graph convolutional layer, the quantum graph pooling layer, and the network optimization. In particular, the trainability of the QSGCN model is analyzed through discussions on the barren plateau phenomenon. Simulation results from various types of graph data are presented to demonstrate the learning, generalization, and robustness capabilities of the proposed quantum neural network (QNN) model.

DOI: 10.1109/tnnls.2024.3382174

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