Variational optimization in the AI era

The variational method has been a cornerstone approach to tackling the quantum many-body problem since the beginnings of quantum mechanics. Throughout this history, wave-functions have grown in number of parameters and generality. The eventual conclusion to this arc is to consider the variational space of all computer programs. Using tools and inspiration from AI, we have developed an approach to represent this class (computational graph states); a novel way to optimize tens of thousands of parameters within this space (supervised wave-function optimization); and multiple novel variational ansatz (neural net backflow, etc). We will describe these advancements and our effort to push forward, in the age of AI, the variational approach to the quantum many body problem.