| 引用本文格式: Mei Wan-Li,Xu Jun. Identification of quantum phase transition point in two-component Bose-Einstein condensates based on deep learning [J]. J. At. Mol. Phys., 2024, 41(2): 026009 (in Chinese) [梅万利,徐军. 基于深度学习的两分量BEC中量子相变点的识别 [J]. 原子与分子物理学报, 2024, 41(2): 026009] |
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| 基于深度学习的两分量BEC中量子相变点的识别 |
| Identification of quantum phase transition point in two-component Bose-Einstein condensates based on deep learning |
| 摘要点击 245 全文点击 55 投稿时间：2022-10-09 修订日期：2022-10-25 |
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| DOI编号
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| 中文关键词
量子相变 BEC 深度学习 卷积神经网络 |
| 英文关键词
Quantum phase transition BEC Deep learning Convolutional neural network |
| 基金项目
国家自然科学基金,省市自然科学基金 |
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| 中文摘要
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| 识别物质的相变是物理学研究中一个重要问题。本文采用了一种混淆标签方案的卷积神经网络算法来识别两分量玻色-爱因斯坦凝聚（BEC）中量子相变点，通过计算神经网络输出的准确率，得到W型性能曲线，此性能曲线中间的极大值对应着量子相变的临界点。研究结果表明，深度学习得到的量子相变点与解析计算值吻合度较高。此混淆标签方案的深度学习研究方法可以应用到存在两种相的相变体系。 |
| 英文摘要
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| Recognizing the phase transition of matter is an important problem in physics research. Convolutional neural network algorithm of confusion label scheme is used to identify quantum phase transition point of two-component Bose-Einstein condensates (BEC) in this paper. By calculating the output accuracy of neural network, the W-shape performance curve is obtained. The maximum value in the middle of W-shape performance curve corresponds to the critical point of quantum phase transition. The research results show that the critical point obtained by deep learning is consistent with the analytic calculation results. The deep learning method of confusion label scheme can be applied to the phase transition system of existing two phases. |
