量子卷积神经网络用于预测核电荷半径
Quantum convolutional neural network for predicting nuclear charge radii
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中文总结 AI 辅助
本文首次将混合量子卷积神经网络应用于核电荷半径预测，通过引入变分量子卷积滤波器提取局部相关性，展示了良好的预测精度和稳定性，为核结构数据分析提供了新工具。
中文摘要 AI 辅助
量子机器学习有潜力成为理解复杂核结构的新工具。在本工作中，我们首次将混合量子卷积神经网络（QCNN）应用于核电荷半径预测，旨在探索量子机器学习在核物理数据分析中的可行性。基于经典卷积神经网络（CNN）框架，我们引入一个小型变分量子卷积滤波器作为量子特征映射，以提取核图上局部相关性。QCNN展现出有前景的预测准确性和训练稳定性，并对若干代表性同位素链的电荷半径演化提供了可靠描述。这些结果支持进一步研究用于核结构数据分析的量子卷积架构。
英文摘要
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.
发表机构
- College of Physics, Jilin University（吉林大学物理学院）
- Department of Physics, Graduate School of Science, The University of Tokyo（东京大学理学研究科物理学系）
- RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program（理化学研究所跨学科理论与数学科学项目）
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