2022
DOI: 10.21468/scipostphys.12.1.006
|View full text |Cite
|
Sign up to set email alerts
|

Automatic differentiation applied to excitations with projected entangled pair states

Abstract: The excitation ansatz for tensor networks is a powerful tool for simulating the low-lying quasiparticle excitations above ground states of strongly correlated quantum many-body systems. Recently, the two-dimensional tensor network class of infinite projected entangled-pair states gained new ground state optimization methods based on automatic differentiation, which are at the same time highly accurate and simple to implement. Naturally, the question arises whether these new ideas can also be used to optimize t… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
12
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
5
1

Relationship

2
4

Authors

Journals

citations
Cited by 25 publications
(12 citation statements)
references
References 65 publications
0
12
0
Order By: Relevance
“…The tensor-network renormalization group methods [30,31,[35][36][37] are used to calculate dynamical response functions of the anisotropic Heisenberg model, defined in Eq. ( 1), with J = 1.67 meV and ∆ = 0.95.…”
Section: Methodsmentioning
confidence: 99%
See 4 more Smart Citations
“…The tensor-network renormalization group methods [30,31,[35][36][37] are used to calculate dynamical response functions of the anisotropic Heisenberg model, defined in Eq. ( 1), with J = 1.67 meV and ∆ = 0.95.…”
Section: Methodsmentioning
confidence: 99%
“…A translation invariant PEPS on an infinite lattice is used to represent the ground state with 120 • antiferromagnetic order. An ansatz based on the single-mode approximation [31,[35][36][37] is used to construct the wave functions of excitation states. The local tensors for both the ground state and the excitation states are optimized by minimizing the corresponding energies with the aid of automatic differentiation [30,31].…”
Section: Methodsmentioning
confidence: 99%
See 3 more Smart Citations