Variational adiabatic transport of tensor networks

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Kim, Hyeongjin, Fishman, Matthew T., Sels, Dries
Format: Preprint
Published: 2023
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866914834029740032
author Kim, Hyeongjin
Fishman, Matthew T.
Sels, Dries
author_facet Kim, Hyeongjin
Fishman, Matthew T.
Sels, Dries
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2311_00748
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Variational adiabatic transport of tensor networks
Kim, Hyeongjin
Fishman, Matthew T.
Sels, Dries
Quantum Physics
Strongly Correlated Electrons
Computational Physics
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.
title Variational adiabatic transport of tensor networks
topic Quantum Physics
Strongly Correlated Electrons
Computational Physics
url https://arxiv.org/abs/2311.00748