Machine learning for self-bound quantum systems

Màster Oficial de Ciència i Tecnologia Quàntiques / Quantum Science and Technology, Facultat de Física, Universitat de Barcelona. Curs: 2023-2024. Tutors: Arnau Rios Huguet, Javier Rozalén Sarmiento

Detalles Bibliográficos
Autor: Mosteiro García, Jesús
Tipo de recurso: tesis de maestría
Fecha de publicación:2024
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/215226
Acceso en línea:https://hdl.handle.net/2445/215226
Access Level:acceso abierto
Palabra clave:Xarxes neuronals (Informàtica)
Aprenentatge automàtic
Treballs de fi de màster
Neural networks (Computer science)
Machine learning
Master's thesis
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spelling Machine learning for self-bound quantum systemsMosteiro García, JesúsXarxes neuronals (Informàtica)Aprenentatge automàticTreballs de fi de màsterNeural networks (Computer science)Machine learningMaster's thesisMàster Oficial de Ciència i Tecnologia Quàntiques / Quantum Science and Technology, Facultat de Física, Universitat de Barcelona. Curs: 2023-2024. Tutors: Arnau Rios Huguet, Javier Rozalén SarmientoIn this work we compute the ground-state properties of one-dimensional systems composed of fully-polarized fermions interacting through an attractive Gaussian potential. If the interactions are able to overcome kinetic energy contributions, these particles can become bound without the presence of an external potential. We use Neural Quantum States, a technique which exploits deep neural networks as the ansatz for a Variational Monte Carlo. We adapt a previously presented network architecture, that was designed to enforce fermionic antisymmetry, to also include a mean-centering transformation. This fixes the system at the origin of space, overcoming numerical instabilities. Transfer learning from ansätze trained on systems trapped with an external potential is used to facilitate reaching a global energy minimum. We predict analytically and confirm computationally that, under certain conditions, these systems are fully characterized by their scattering length, giving rise to what we call “1D fermionic halo systems”. Our results suggest that the ground state energy decreases linearly with the amount of fermions and has an inverse quadratic dependence on the scattering lengthRios Huguet, ArnauRozalén Sarmiento, Javier2024info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2445/215226Màster Oficial - Ciència i Tecnologia Quàntiques / Quantum Science and Technologyreponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaIngléscc-by-nc-nd (c) Mosteiro, 2024http://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/2152262026-05-27T06:46:51Z
dc.title.none.fl_str_mv Machine learning for self-bound quantum systems
title Machine learning for self-bound quantum systems
spellingShingle Machine learning for self-bound quantum systems
Mosteiro García, Jesús
Xarxes neuronals (Informàtica)
Aprenentatge automàtic
Treballs de fi de màster
Neural networks (Computer science)
Machine learning
Master's thesis
title_short Machine learning for self-bound quantum systems
title_full Machine learning for self-bound quantum systems
title_fullStr Machine learning for self-bound quantum systems
title_full_unstemmed Machine learning for self-bound quantum systems
title_sort Machine learning for self-bound quantum systems
dc.creator.none.fl_str_mv Mosteiro García, Jesús
author Mosteiro García, Jesús
author_facet Mosteiro García, Jesús
author_role author
dc.contributor.none.fl_str_mv Rios Huguet, Arnau
Rozalén Sarmiento, Javier
dc.subject.none.fl_str_mv Xarxes neuronals (Informàtica)
Aprenentatge automàtic
Treballs de fi de màster
Neural networks (Computer science)
Machine learning
Master's thesis
topic Xarxes neuronals (Informàtica)
Aprenentatge automàtic
Treballs de fi de màster
Neural networks (Computer science)
Machine learning
Master's thesis
description Màster Oficial de Ciència i Tecnologia Quàntiques / Quantum Science and Technology, Facultat de Física, Universitat de Barcelona. Curs: 2023-2024. Tutors: Arnau Rios Huguet, Javier Rozalén Sarmiento
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/215226
url https://hdl.handle.net/2445/215226
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv cc-by-nc-nd (c) Mosteiro, 2024
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by-nc-nd (c) Mosteiro, 2024
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Màster Oficial - Ciència i Tecnologia Quàntiques / Quantum Science and Technology
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812455

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