Second Renormalization of Tensor-Network States

TL;DR

This paper introduces a second renormalization group method for tensor-network states that significantly reduces truncation errors, enabling more accurate and efficient computation of physical quantities in classical and quantum models.

Contribution

The paper presents a novel second renormalization group approach that improves the accuracy of tensor-network state calculations over previous methods.

Findings

Reduces truncation errors in tensor renormalization group methods.

Allows precise computation of physical quantities in classical and quantum tensor-network models.

Enhances efficiency of tensor-network state analysis.

Abstract

We propose a second renormalization group method to handle the tensor-network states or models. This method reduces dramatically the truncation error of the tensor renormalization group. It allows physical quantities of classical tensor-network models or tensor-network ground states of quantum systems to be accurately and efficiently determined.