Observing a topological phase transition with deep neural networks from experimental images of ultracold atoms.

@article{Zhao2022ObservingAT,
  title={Observing a topological phase transition with deep neural networks from experimental images of ultracold atoms.},
  author={Entong Zhao and Ting Hin Mak and Chengdong He and Zejian Ren and Kangsa Pak and Yu-Jun Liu and Gyu-Boong Jo},
  journal={Optics express},
  year={2022},
  volume={30 21},
  pages={
          37786-37794
        }
}
Although classifying topological quantum phases have attracted great interests, the absence of local order parameter generically makes it challenging to detect a topological phase transition from experimental data. Recent advances in machine learning algorithms enable physicists to analyze experimental data with unprecedented high sensitivities, and identify quantum phases even in the presence of unavoidable noises. Here, we report a successful identification of topological phase transitions… 

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