Tensor-network toolbox for probing dynamics of non-Abelian gauge theories

Tensor-network methods enable probing dynamics of strongly interacting quantum many-body systems, including gauge theories, via Hamiltonian simulation, hence bypassing sign problems. They also have the potential to inform efficient quantum-simulation algorithms of the same theories. We develop and benchmark a matrix-product-state ansatz for the SU(2) lattice gauge theory using the loop-string-hadron formulation. This formulation has been demonstrated to be advantageous in Hamiltonian simulation of non-Abelian gauge theories. It is applicable to both SU(2) and SU(3) gauge groups, to periodic and open boundary conditions, and to 1+1 and higher dimensions. In this work, we report on progress in computing static and dynamical observables in a SU(2) gauge theory in (1+1)D, pushing the boundary of existing studies.

Authors

Publication Details

Published
2025-02-15
DOI
https://doi.org/10.22323/1.466.0472
Primary Topic
Computational Physics and Python Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Tensor-network toolbox for probing dynamics of non-Abelian gauge theories

Indrakshi Raychowdhury, Navya Gupta, Zohreh Davoudi, Aniruddha Bapat et al.
Computational Physics and Python Applications
article

Tensor-network toolbox for probing dynamics of non-Abelian gauge theories

Indrakshi Raychowdhury, Navya Gupta, Zohreh Davoudi, Aniruddha Bapat, Jesse R. Stryker, Saurabh V. Kadam, Emil Mathew
article en

Abstract

Tensor-network methods enable probing dynamics of strongly interacting quantum many-body systems, including gauge theories, via Hamiltonian simulation, hence bypassing sign problems. They also have the potential to inform efficient quantum-simulation algorithms of the same theories. We develop and benchmark a matrix-product-state ansatz for the SU(2) lattice gauge theory using the loop-string-hadron formulation. This formulation has been demonstrated to be advantageous in Hamiltonian simulation of non-Abelian gauge theories. It is applicable to both SU(2) and SU(3) gauge groups, to periodic and open boundary conditions, and to 1+1 and higher dimensions. In this work, we report on progress in computing static and dynamical observables in a SU(2) gauge theory in (1+1)D, pushing the boundary of existing studies.

Openalex Percentile: Top 99%
Computational Physics and Python Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Tensor-network toolbox for probing dynamics of non-Abelian gauge theories — Indrakshi Raychowdhury, Navya Gupta, et al. · (2025) | TGRS Research Map | TGRS