Quantum Generative Adversarial Networks: Improving Dynamics Simulation with an Ancilla Qubit

Màster Oficial de Ciència i Tecnologia Quàntiques / Quantum Science and Technology, Facultat de Física, Universitat de Barcelona. Curs: 2024-2025. Tutors:Some Sankar Bhattacharya, Ayaka Usui

Detalhes bibliográficos
Autor: Abad López, Guillermo
Tipo de documento: dissertação
Data de publicação:2025
País:España
Recursos:Universidad de Barcelona
Repositório:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/223223
Acesso em linha:https://hdl.handle.net/2445/223223
Access Level:Acceso aberto
Palavra-chave:Xarxa generativa antagònica
Qbit
Treballs de fi de màster
Generative adversarial network
Qubit
Master's thesis
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spelling Quantum Generative Adversarial Networks: Improving Dynamics Simulation with an Ancilla QubitAbad López, GuillermoXarxa generativa antagònicaQbitTreballs de fi de màsterGenerative adversarial networkQubitMaster's thesisMàster Oficial de Ciència i Tecnologia Quàntiques / Quantum Science and Technology, Facultat de Física, Universitat de Barcelona. Curs: 2024-2025. Tutors:Some Sankar Bhattacharya, Ayaka UsuiSimulating complex quantum systems remains a critical challenge, as conventional quantum techniques– such as those based on the Suzuki–Trotter decomposition—often result in deep circuits that demand substantial computational resources. Quantum Generative Adversarial Networks (QGANs) offer a promising alternative by learning the time evolution of target Hamiltonian using significantly fewer gates. However, standard QGAN architectures commonly suffer from unstable convergence and learning plateaus in the loss landscape, which hinder training and prevent the generator from achieving high-fidelity solutions. To address these limitations, we propose augmenting the generator with an ancilla qubit, expanding the learning space, and providing additional degrees of freedom that enable training to progress when the model becomes trapped in certain regions of the loss landscape. In this work, we investigate the effect of incorporating an ancilla under various connectivity topologies and at different stages of training, in order to perturb the optimization landscape and aid the generator overcome problematic training cases Simulation results demonstrate that ancilla-assisted QGANs successfully escape learning plateaus and other non-convergent behaviours, particularly when the ancilla’s connectivity links distant regions of the ansatz. Notably, the optimized fidelity overall improves when the ancilla is introduced mid-way through the training.Bhattacharya, S.S.Usui, A.2025info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2445/223223Màster Oficial - Ciència i Tecnologia Quàntiques / Quantum Science and Technologyreponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaIngléscc-by-nc-nd (c) Abad, 2025http://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/2232232026-05-27T06:46:51Z
dc.title.none.fl_str_mv Quantum Generative Adversarial Networks: Improving Dynamics Simulation with an Ancilla Qubit
title Quantum Generative Adversarial Networks: Improving Dynamics Simulation with an Ancilla Qubit
spellingShingle Quantum Generative Adversarial Networks: Improving Dynamics Simulation with an Ancilla Qubit
Abad López, Guillermo
Xarxa generativa antagònica
Qbit
Treballs de fi de màster
Generative adversarial network
Qubit
Master's thesis
title_short Quantum Generative Adversarial Networks: Improving Dynamics Simulation with an Ancilla Qubit
title_full Quantum Generative Adversarial Networks: Improving Dynamics Simulation with an Ancilla Qubit
title_fullStr Quantum Generative Adversarial Networks: Improving Dynamics Simulation with an Ancilla Qubit
title_full_unstemmed Quantum Generative Adversarial Networks: Improving Dynamics Simulation with an Ancilla Qubit
title_sort Quantum Generative Adversarial Networks: Improving Dynamics Simulation with an Ancilla Qubit
dc.creator.none.fl_str_mv Abad López, Guillermo
author Abad López, Guillermo
author_facet Abad López, Guillermo
author_role author
dc.contributor.none.fl_str_mv Bhattacharya, S.S.
Usui, A.
dc.subject.none.fl_str_mv Xarxa generativa antagònica
Qbit
Treballs de fi de màster
Generative adversarial network
Qubit
Master's thesis
topic Xarxa generativa antagònica
Qbit
Treballs de fi de màster
Generative adversarial network
Qubit
Master's thesis
description Màster Oficial de Ciència i Tecnologia Quàntiques / Quantum Science and Technology, Facultat de Física, Universitat de Barcelona. Curs: 2024-2025. Tutors:Some Sankar Bhattacharya, Ayaka Usui
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/223223
url https://hdl.handle.net/2445/223223
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv cc-by-nc-nd (c) Abad, 2025
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by-nc-nd (c) Abad, 2025
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
_version_ 1869409288985772032
score 15,812455

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