Gradient optimization of finite projected entangled pair states

TL;DR

This paper introduces an efficient gradient optimization method combining time evolution, Monte Carlo sampling, and parallel computing to improve PEPS ground state calculations for larger bond dimensions, achieving high accuracy.

Contribution

The paper presents a novel combination of techniques enabling PEPS ground state optimization at larger bond dimensions without symmetry assumptions.

Findings

Successfully optimized PEPS with bond dimension up to D=10.

Achieved high accuracy in benchmark tests on the J1-J2 model.

Demonstrated scalability with massive parallel computing.

Abstract

The projected entangled pair states (PEPS) methods have been proved to be powerful tools to solve the strongly correlated quantum many-body problems in two-dimension. However, due to the high computational scaling with the virtual bond dimension $D$, in a practical application PEPS are often limited to rather small bond dimensions, which may not be large enough for some highly entangled systems, for instance, the frustrated systems. The optimization of the ground state using time evolution method with simple update scheme may go to a larger bond dimension. However, the accuracy of the rough approximation to the environment of the local tensors is questionable. Here, we demonstrate that combining the time evolution method with simple update, Monte Carlo sampling techniques and gradient optimization will offer an efficient method to calculate the PEPS ground state. By taking the advantages of massive parallel computing, we can study the quantum systems with larger bond dimensions up to $D$=10 without resorting to any symmetry. Benchmark tests of the method on the $J_1$-$J_2$ model give impressive accuracy compared with exact results.