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This work presents a quantum convolutional neural network (QCNN) for theclassification of high energy physics events. The proposed model is testedusing a simulated dataset from the Deep Underground Neutrino Experiment. Theproposed architecture demonstrates the quantum advantage of learning fasterthan the classical convolutional neural networks (CNNs) under a similar numberof parameters. In addition to faster convergence, the QCNN achieves greatertest accuracy compared to CNNs. Based on experimental results, it is apromising direction to study the application of QCNN and other quantum machinelearning models in high energy physics and additional scientific fields.

Quantum Convolutional Neural Networks for High Energy Physics Data Analysis