Quantum convolutional neural network for predicting nuclear charge radii
Abstract
Quantum machine learning has the potential to become a new tool for understanding complex nuclear structures. In this work, we apply a hybrid quantum convolutional neural network (QCNN) to nuclear charge-radius prediction for the first time, aiming to explore the feasibility of quantum machine learning in nuclear-physics data analysis. Based on a classical convolutional neural network (CNN) framework, a small variational quantum convolutional filter is introduced as a quantum feature map to extract local correlations on the nuclear chart. The QCNN shows promising predictive accuracy and training stability, and provides a reliable description of the charge-radius evolution along several representative isotopic chains. These results support further investigation of quantum convolutional architectures for nuclear-structure data analysis.
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BibTeX
@misc{wu2026quantum,
title = {{Quantum convolutional neural network for predicting nuclear charge radii}},
author = {Jinzhe Wu and Jianping Zhao and Tianshuai Shang and Yanhua Lu and Yundong Wang and Jian Li and Haozhao Liang},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.12393},
eprint = {2609.12393},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.12393},
}
RIS
TY - UNPB
TI - Quantum convolutional neural network for predicting nuclear charge radii
AU - Jinzhe Wu
AU - Jianping Zhao
AU - Tianshuai Shang
AU - Yanhua Lu
AU - Yundong Wang
AU - Jian Li
AU - Haozhao Liang
PY - 2026
DA - 2026/09/11
PB - arXiv
DO - 10.48550/arxiv.2609.12393
UR - https://arxiv.org/abs/2609.12393
AB - Quantum machine learning has the potential to become a new tool for understanding complex nuclear structures. In this work, we apply a hybrid quantum convolutional neural network (QCNN) to nuclear charge-radius prediction for the first time, aiming to explore the feasibility of quantum machine learning in nuclear-physics data analysis. Based on a classical convolutional neural network (CNN) framework, a small variational quantum convolutional filter is introduced as a quantum feature map to extract local correlations on the nuclear chart. The QCNN shows promising predictive accuracy and training stability, and provides a reliable description of the charge-radius evolution along several representative isotopic chains. These results support further investigation of quantum convolutional architectures for nuclear-structure data analysis.
ER -