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Quantum Machine Learning
Quantum Convolutional Neural Networks for High Energy Physics Data Analysis
arXiv
Authors: Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang, Haiwang Yu, Shinjae Yoo
Year
2020
Paper ID
18184
Status
Preprint
Abstract Read
~2 min
Abstract Words
98
Citations
N/A
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.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2020 reference point for readers tracking recent quantum research.
- This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events.
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