Data-Centric Learning of Quantum Many-Body States

I will discuss how to learn the nature of quantum many-body states encoded in the data through data-centric approaches using machine learning. Of particular interest are cases when the fingerprint of a given unusually complex state is not obvious from the outset. Using thoughtfully designed strategies that leverage key mechanism of given machine learning algorithm in conjunction one can gain new insight into unusual states such as gapless spin liquid or topological order in a mixed state. I will present cases s that led to fruitful insights and started to shape an approach to data sets of the new era.