1 citations · 1 across the 1 of their papers we have counts for
2 papers
cond-mat.supr-con2021★ 1 cited
Deep learning of topological phase transitions from entanglement aspects: An unsupervised way
Yuan-Hong Tsai, Kuo-Feng Chiu, Yong-Cheng Lai +5
Machine learning techniques have been shown to be effective to recognize different phases of matter and produce phase diagrams in the parameter space interested, while they usually…
cond-mat.supr-con2021
Deep learning of topological phase transitions from entanglement aspects for two-dimensional chiral p-wave superconductors
Ming-Chiang Chung, Tsung-Pao Cheng, Guang-Yu Huang +1
Applying deep learning to investigate topological phase transitions (TPTs) becomes a useful method due to not only its ability to recognize patterns but also its statistical excell…