  Classical data with graph structure always exists when dealing with many
real-world problems. In parallel, quantum data with graph structure also need
to be investigated since they are always produced by common quantum data
sources.In this paper, we build a hybrid quantum-classical neural network with
deep residual learning (Res-HQCNN) with graph-structured quantum data.
Specifically, based on this special graph-structured quantum data, we first
find suitable cost functions for Res-HQCNN model to learn semisupervised
quantum data with graphs. Then, we present the training algorithm of Res-HQCNN
for graph-structured training data in detail. Next, in order to show the
learning ability of Res-HQCNN,we perform extensive experiments to show that the
using of information about graph structures in quantum data can lead to better
learning efficiency compared with the state-of-the-art model. At the same time,
we also design comparable experiments to explain that the using of residual
block structure can help deeper quantum neural networks learn graph-structured
quantum data faster and better.
