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.
@article{arxiv.2012.12177,
title = {Quantum Convolutional Neural Networks for High Energy Physics Data Analysis},
author = {Samuel Yen-Chi Chen and Tzu-Chieh Wei and Chao Zhang and Haiwang Yu and Shinjae Yoo},
journal= {arXiv preprint arXiv:2012.12177},
year = {2020}
}