  At present, there are a large number of quantum neural network models to deal
with Euclidean spatial data, while little research have been conducted on
non-Euclidean spatial data. In this paper, we propose a novel quantum graph
convolutional neural network (QGCN) model based on quantum parametric circuits
and utilize the computing power of quantum systems to accomplish graph
classification tasks in traditional machine learning. The proposed QGCN model
has a similar architecture as the classical graph convolutional neural
networks, which can illustrate the topology of the graph type data and
efficiently learn the hidden layer representation of node features as well.
Numerical simulation results on a graph dataset demonstrate that the proposed
model can be effectively trained and has good performance in graph level
classification tasks.
