Work

Quantum convolutional neural network for predicting nuclear charge radii

Year: 2026

Type: preprint

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.

Source: arXiv (Cornell University)

Authors: Wu, Jinzhe, Zhao, Jianping, Shang, Tianshuai, Lu, Yanhua, Wang, Yundong, Li, Jian, Liang, Haozhao

Institutions: Jilin University, Tokyo University of Science, The University of Tokyo

Topic: Nuclear physics research studies

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Japan Society for the Promotion of Science

Awards: G2560603643, r-process nucleosynthesis and role of deformed nuclei

OpenAlex ID: W7212919623

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