Quantum Convolutional Neural Networks for High Energy Physics Data Analysis

• URL: http://arxiv.org/abs/2012.12177v1
• Date: Tue, 22 Dec 2020 17:14:47 GMT
• Title: Quantum Convolutional Neural Networks for High Energy Physics Data Analysis
• Authors: Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang, Haiwang Yu, Shinjae Yoo
• Abstract summary: This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed architecture demonstrates the quantum advantage of learning faster than the classical convolutional neural networks (CNNs) under a similar number of parameters.
• Score: 7.0132255816377445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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