  Quantum neural networks (QNNs), an interdisciplinary field of quantum
computing and machine learning, have attracted tremendous research interests
due to the specific quantum advantages. Despite lots of efforts developed in
computer vision domain, one has not fully explored QNNs for the real-world
graph property classification and evaluated them in the quantum device. To
bridge the gap, we propose quantum graph convolutional networks (QuanGCN),
which learns the local message passing among nodes with the sequence of
crossing-gate quantum operations. To mitigate the inherent noises from modern
quantum devices, we apply sparse constraint to sparsify the nodes' connections
and relieve the error rate of quantum gates, and use skip connection to augment
the quantum outputs with original node features to improve robustness. The
experimental results show that our QuanGCN is functionally comparable or even
superior than the classical algorithms on several benchmark graph datasets. The
comprehensive evaluations in both simulator and real quantum machines
demonstrate the applicability of QuanGCN to the future graph analysis problem.
