Machine learning topological states

Abstract

Machine learning, the core of artificial intelligence and data science, is a very active field, with vast applications throughout science and technology. Recently, machine learning techniques have been adopted to tackle intricate quantum many-body problems and phase transitions. In this work, the authors construct exact mappings from exotic quantum states to machine learning network models. This work shows for the first time that the restricted Boltzmann machine can be used to study both symmetry-protected topological phases and intrinsic topological order. The exact results are expected to provide a substantial boost to the field of machine learning of phases of matter.