Fast convergence of imaginary time evolution tensor network algorithms by recycling the environment

TL;DR

This paper introduces an environment recycling scheme for tensor network algorithms that significantly accelerates imaginary time evolution by reusing the environment, especially near convergence, reducing computational costs in 1D and 2D systems.

Contribution

The authors propose a novel environment recycling method that leverages gauge invariance to reuse environments in tensor network algorithms, enhancing efficiency in ground state computations.

Findings

Substantial computational savings in 1D and 2D tensor network simulations.

Effective in computing ground states of the quantum Ising model.

Applicable to both MPS and PEPS tensor network states.

Abstract

We propose an environment recycling scheme to speed up a class of tensor network algorithms that produce an approximation to the ground state of a local Hamiltonian by simulating an evolution in imaginary time. Specifically, we consider the time-evolving block decimation (TEBD) algorithm applied to infinite systems in 1D and 2D, where the ground state is encoded, respectively, in a matrix product state (MPS) and in a projected entangled-pair state (PEPS). An important ingredient of the TEBD algorithm (and a main computational bottleneck, especially with PEPS in 2D) is the computation of the so-called environment, which is used to determine how to optimally truncate the bond indices of the tensor network so that their dimension is kept constant. In current algorithms, the environment is computed at each step of the imaginary time evolution, to account for the changes that the time evolution introduces in the many-body state represented by the tensor network. Our key insight is that close to convergence, most of the changes in the environment are due to a change in the choice of gauge in the bond indices of the tensor network, and not in the many-body state. Indeed, a consistent choice of gauge in the bond indices confirms that the environment is essentially the same over many time steps and can thus be re-used, leading to very substantial computational savings. We demonstrate the resulting approach in 1D and 2D by computing the ground state of the quantum Ising model in a transverse magnetic field.