Talk / Overview
Variational methods have proven to be excellent tools to approximate ground states of complex
many body Hamiltonians. Generic tools like neural networks are extremely powerful, but their
parameters are not necessarily physically motivated. Thus, an efficient parametrization of the
wave-function can become challenging. In this talk I will introduce a neural-network based variational
ansatz that retains the flexibility of these generic methods while allowing for a tunability with respect
to the relevant physics of the system. We illustrate the success of this
approach on topological, long-range correlated and frustrated models.