Monte Carlo simulation with Tensor Network States

TL;DR

This paper demonstrates that Monte Carlo sampling combined with tensor network states can efficiently compute ground state properties of large quantum systems, achieving high accuracy and improving previous results.

Contribution

It introduces a method to use Monte Carlo sampling with tensor network states for efficient expectation value calculations of large systems.

Findings

Accurately computed ground state energy and magnetization for large lattices.

Reduced magnetization errors compared to previous methods.

Achieved finite D extrapolation close to exact values.

Abstract

It is demonstrated that Monte Carlo sampling can be used to efficiently extract the expectation value of projected entangled pair states with large virtual bond dimension. We use the simple update rule introduced by Xiang et al. to obtain the tensors describing the ground state wavefunction of the antiferromagnetic Heisenberg model and evaluate the finite size energy and staggered magnetization for square lattices with periodic boundary conditions of sizes up to L=16 and virtual bond dimensions up to D=16. The finite size magnetization errors are 0.003(2) and 0.013(2) at D=16 for a system of size L=8,16 respectively. Finite D extrapolation provides exact finite size magnetization for L=8, and reduces the magnetization error to 0.005(3) for L=16, significantly improving the previous state of the art results.