From tensor-network quantum states to tensorial recurrent neural networks

Abstract

A recurrent neural network with a linear memory update is proposed to exactly represent any matrix product state (MPS) and is further generalized to 2D lattices using a multilinear memory update. It supports perfect sampling and wave-function evaluation in polynomial time, provides exact representation of an area law of entanglement entropy, and outperforms MPS by orders of magnitude in parameter efficiency.