| Autor: |
Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang, Haiwang Yu, Shinjae Yoo |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Physical Review Research, Vol 4, Iss 1, p 013231 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2643-1564 |
| DOI: |
10.1103/PhysRevResearch.4.013231 |
| Popis: |
This paper 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 quantum architecture demonstrates an advantage of learning faster than the classical convolutional neural networks (CNNs) under a similar number of parameters. In addition to the faster convergence, the QCNN achieves a greater test accuracy compared to CNNs. Based on our results from numerical simulations, it is a promising direction to apply QCNN and other quantum machine learning models to high energy physics and other scientific fields. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
