Equivalence of restricted Boltzmann machines and tensor network states

The restricted Boltzmann machine is a fundamental building block of deep learning. The authors demonstrate its equivalence with tensor network states with explicit mappings, thus drawing a constructive connection between deep learning and quantum physics. On one side, deep learning approaches can be used to study novel states of matter. In return, investigations of tensor network states and their expressibility can be adapted to guide neural network architecture design.