  The high energy physics (HEP) community has a long history of dealing with
large-scale datasets. To manage such voluminous data, classical machine
learning and deep learning techniques have been employed to accelerate physics
discovery. Recent advances in quantum machine learning (QML) have indicated the
potential of applying these techniques in HEP. However, there are only limited
results in QML applications currently available. In particular, the challenge
of processing sparse data, common in HEP datasets, has not been extensively
studied in QML models. This research provides a hybrid quantum-classical graph
convolutional network (QGCNN) for learning HEP data. The proposed framework
demonstrates an advantage over classical multilayer perceptron and
convolutional neural networks in the aspect of number of parameters. Moreover,
in terms of testing accuracy, the QGCNN shows comparable performance to a
quantum convolutional neural network on the same HEP dataset while requiring
less than $50\%$ of the parameters. Based on numerical simulation results,
studying the application of graph convolutional operations and other QML models
may prove promising in advancing HEP research and other scientific fields.
