Bibliometric Analysis and Overview of Matrix Product States in the Bose-Hubbard Model

Context: Quantum many-body systems have been a prominent topic over the past two decades, underpinning advancements in superconductors, ultracold atoms, and quantum computing, among other fields. This bibliometric analysis explores key concepts, influential authors, and the current significance of a...

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Main Authors: Juan Sebastián Gómez, Karen Cecilia Rodríguez
Format: Article
Language:Spanish
Published: Universidad Distrital Francisco José de Caldas 2025-03-01
Series:Ingeniería
Subjects:
Online Access:https://revistas.udistrital.edu.co/index.php/reving/article/view/22292
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author Juan Sebastián Gómez
Karen Cecilia Rodríguez
author_facet Juan Sebastián Gómez
Karen Cecilia Rodríguez
author_sort Juan Sebastián Gómez
collection DOAJ
description Context: Quantum many-body systems have been a prominent topic over the past two decades, underpinning advancements in superconductors, ultracold atoms, and quantum computing, among other fields. This bibliometric analysis explores key concepts, influential authors, and the current significance of a powerful family of algorithms in computational physics, i.e., density matrix renormalization group (DMRG) algorithms. Special emphasis is placed on the use of tensor product states in developing classical simulations of quantum systems. Method: This paper presents a literature review sourced from the SCOPUS database. It analyzes trends and approaches related to uncertainty in numerical developments for quantum many-body systems, with a focus on the Bose-Hubbard Model, in order to better understand the imposition of additional constraints to ensure the validity of the results. Results: The increasing number of publications on this topic over the last decade indicates a growing interest in solutions for many-body quantum systems, driven by promising advances in superconductive materials, quantum computing, and other impactful areas. Conclusions: This work explored essential foundational works to help beginners understand a well-established technique that aims to overcome the limitations of classical computing. The use of matrix product states in DMRG algorithms is gaining significant traction in various fields, including quantum computing, machine learning, and statistical mechanics, with the purpose of addressing the challenges related to quantum many-body systems.
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issn 0121-750X
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language Spanish
publishDate 2025-03-01
publisher Universidad Distrital Francisco José de Caldas
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spelling doaj-art-112eea1cc5a342f59c19a2a9afff53ae2025-08-20T03:07:04ZspaUniversidad Distrital Francisco José de CaldasIngeniería0121-750X2344-83932025-03-01301e22292e2229210.14483/23448393.2229221130Bibliometric Analysis and Overview of Matrix Product States in the Bose-Hubbard ModelJuan Sebastián Gómez0https://orcid.org/0009-0002-3206-9320Karen Cecilia Rodríguez1https://orcid.org/0000-0003-2860-9208Universidad del Valle Universidad del Valle Context: Quantum many-body systems have been a prominent topic over the past two decades, underpinning advancements in superconductors, ultracold atoms, and quantum computing, among other fields. This bibliometric analysis explores key concepts, influential authors, and the current significance of a powerful family of algorithms in computational physics, i.e., density matrix renormalization group (DMRG) algorithms. Special emphasis is placed on the use of tensor product states in developing classical simulations of quantum systems. Method: This paper presents a literature review sourced from the SCOPUS database. It analyzes trends and approaches related to uncertainty in numerical developments for quantum many-body systems, with a focus on the Bose-Hubbard Model, in order to better understand the imposition of additional constraints to ensure the validity of the results. Results: The increasing number of publications on this topic over the last decade indicates a growing interest in solutions for many-body quantum systems, driven by promising advances in superconductive materials, quantum computing, and other impactful areas. Conclusions: This work explored essential foundational works to help beginners understand a well-established technique that aims to overcome the limitations of classical computing. The use of matrix product states in DMRG algorithms is gaining significant traction in various fields, including quantum computing, machine learning, and statistical mechanics, with the purpose of addressing the challenges related to quantum many-body systems.https://revistas.udistrital.edu.co/index.php/reving/article/view/22292matrix product statesdensity matrix renormalization groupstrongly correlated systemsbose-hubbard modeltensor networks
spellingShingle Juan Sebastián Gómez
Karen Cecilia Rodríguez
Bibliometric Analysis and Overview of Matrix Product States in the Bose-Hubbard Model
Ingeniería
matrix product states
density matrix renormalization group
strongly correlated systems
bose-hubbard model
tensor networks
title Bibliometric Analysis and Overview of Matrix Product States in the Bose-Hubbard Model
title_full Bibliometric Analysis and Overview of Matrix Product States in the Bose-Hubbard Model
title_fullStr Bibliometric Analysis and Overview of Matrix Product States in the Bose-Hubbard Model
title_full_unstemmed Bibliometric Analysis and Overview of Matrix Product States in the Bose-Hubbard Model
title_short Bibliometric Analysis and Overview of Matrix Product States in the Bose-Hubbard Model
title_sort bibliometric analysis and overview of matrix product states in the bose hubbard model
topic matrix product states
density matrix renormalization group
strongly correlated systems
bose-hubbard model
tensor networks
url https://revistas.udistrital.edu.co/index.php/reving/article/view/22292
work_keys_str_mv AT juansebastiangomez bibliometricanalysisandoverviewofmatrixproductstatesinthebosehubbardmodel
AT karenceciliarodriguez bibliometricanalysisandoverviewofmatrixproductstatesinthebosehubbardmodel