Unbiased Monte Carlo for the age of tensor networks

TL;DR

Tensor Network Monte Carlo (TNMC) introduces an unbiased sampling method for tensor networks, reducing statistical fluctuations and eliminating variational bias, thus enhancing accuracy in physical and statistical computations.

Contribution

The paper presents a novel unbiased Monte Carlo approach for tensor networks that improves accuracy and reduces bias compared to traditional tensor network methods.

Findings

Achieves low statistical fluctuations in tensor network sampling.

Eliminates variational bias inherent in tensor network techniques.

Compatible with various tensor renormalization methods.

Abstract

A new unbiased Monte Carlo technique called Tensor Network Monte Carlo (TNMC) is introduced based on sampling all possible renormalizations (or course-grainings) of tensor networks, in this case matrix-product states. Tensor networks are a natural language for expressing a wide range of discrete physical and statistical problems, such as classical and quantum systems on a lattice at thermal equilibrium. By simultaneously sampling multiple degrees of freedom associated with each bond of the tensor network (and its renormalized form), we can achieve unprecedented low levels of statistical fluctuations which simultaneously parallel the impressive accuracy scaling of tensor networks while avoiding completely the variational bias inherent to those techniques, even with small bond dimensions. The resulting technique is essentially an aggressive multi-sampling technique that can account for the great majority of the partition function in a single sample. The method is quite general and can be combined with a variety of tensor renormalization techniques appropriate to different geometries and dimensionalities.