A beginner’s view on Stochastic Reconfiguration
Stochastic Reconfiguration is a highly relevant technique for machine learning studies of quantum mechanical optimization problems, in particular for Monte Carlo approaches in many-body simulations. In this talk, Daniel will introduce the topic starting from its classical motivation of the Fisher Information metric to its analog for quantum sistems, the Fubini Study metric. The idea is to give a geometrical and intuitive explanation of how it works with some hints from information theory.
Relevant Material