Variational adiabatic transport of tensor networks

TL;DR

This paper introduces a tensor network method for adiabatic transport using the adiabatic gauge potential, enhancing the efficiency and reliability of finding ground states near quantum critical points.

Contribution

The paper presents a novel tensor network approach to construct the adiabatic gauge potential as a matrix product operator for improved state transport and optimization.

Findings

Faster and more reliable computation of ground states near criticality.

Automated step size adjustment and critical point detection.

Successful transport through quantum critical points.

Abstract

We discuss a tensor network method for constructing the adiabatic gauge potential -- the generator of adiabatic transformations -- as a matrix product operator, which allows us to adiabatically transport matrix product states. Adiabatic evolution of tensor networks offers a wide range of applications, of which two are explored in this paper: improving tensor network optimization and scanning phase diagrams. By efficiently transporting eigenstates to quantum criticality and performing intermediary density matrix renormalization group (DMRG) optimizations along the way, we demonstrate that we can compute ground and low-lying excited states faster and more reliably than a standard DMRG method at or near quantum criticality. We demonstrate a simple automated step size adjustment and detection of the critical point based on the norm of the adiabatic gauge potential. Remarkably, we are able to reliably transport states through the critical point of the models we study.