Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions
作者: Tomi OhtsukiTomohiro Mano
刊名: Journal of the Physical Society of Japan, 2020, Vol.89 (2)
来源数据库: The physical Society of Japan
DOI: 10.7566/JPSJ.89.022001
原始语种摘要: Applications of neural networks to condensed matter physics are becoming popular and beginning to be well accepted. Obtaining and representing the ground and excited state wave functions are examples of such applications. Another application is analyzing the wave functions and determining their quantum phases. Here, we review the recent progress of using the multilayer convolutional neural network, so-called deep learning, to determine the quantum phases in random electron systems. After training the neural network by the supervised learning of wave functions in restricted parameter regions in known phases, the neural networks can determine the phases of the wave functions in wide parameter regions in unknown phases; hence, the phase diagrams are obtained. We demonstrate the validity and...
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  • topological 拓扑的
  • applications 应用程序
  • representing 表示
  • neural 神经系统的
  • unknown 不知道的
  • demonstrate 说明
  • popular 普及
  • drawing 绘图
  • analyzing 检测
  • convolutional 卷积