Generative Modeling via Tree Tensor Network States

Xun Tang, Yoonhaeng Hur, Yuehaw Khoo, Lexing Ying

Abstract: In this paper, we present a density estimation framework based on tree tensor-network states. The proposed method consists of determining the tree topology with Chow-Liu algorithm, and obtaining a linear system of equations that defines the tensor-network components via sketching techniques. Novel choices of sketch functions are developed in order to consider graphical models that contain loops. Sample complexity guarantees are provided and further corroborated by numerical experiments.