  This work presents a quantum convolutional neural network (QCNN) for the
classification of high energy physics events. The proposed model is tested
using a simulated dataset from the Deep Underground Neutrino Experiment. The
proposed architecture demonstrates the quantum advantage of learning faster
than the classical convolutional neural networks (CNNs) under a similar number
of parameters. In addition to faster convergence, the QCNN achieves greater
test accuracy compared to CNNs. Based on experimental results, it is a
promising direction to study the application of QCNN and other quantum machine
learning models in high energy physics and additional scientific fields.
