  Numerical approaches to the correlated electron problem have achieved
considerable success, yet are still constrained by several bottlenecks,
including high order polynomial or exponential scaling in system size, long
autocorrelation times, challenges in recognizing novel phases, and the Fermion
sign problem. Methods in machine learning (ML), artificial intelligence, and
data science promise to help address these limitations and open up a new
frontier in strongly correlated quantum system simulations. In this paper, we
review some of the progress in this area. We begin by examining these
approaches in the context of classical models, where their underpinnings and
application can be easily illustrated and benchmarked. We then discuss cases
where ML methods have enabled scientific discovery. Finally, we will examine
their applications in accelerating model solutions in state-of-the-art quantum
many-body methods like quantum Monte Carlo and discuss potential future
research directions.

Strongly Correlated Electrons