Solving the Quantum Many-Body Lindbladian Learning Problem With Neural Differential Equations

Màster Oficial de Ciència i Tecnologia Quàntiques / Quantum Science and Technology, Facultat de Física, Universitat de Barcelona. Curs: 2024-2025. Tutors: Timothy Heightman, Edward Jiang

Detalles Bibliográficos
Autor: Aseguinolaza Gallo, Roman
Tipo de recurso: tesis de maestría
Fecha de publicación:2025
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/223226
Acceso en línea:https://hdl.handle.net/2445/223226
Access Level:acceso abierto
Palabra clave:Equacions diferencials ordinàries
Aprenentatge automàtic
Treballs de fi de màster
Ordinary differential equations
Machine learning
Master's thesis
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spelling Solving the Quantum Many-Body Lindbladian Learning Problem With Neural Differential EquationsAseguinolaza Gallo, RomanEquacions diferencials ordinàriesAprenentatge automàticTreballs de fi de màsterOrdinary differential equationsMachine learningMaster's thesisMàster Oficial de Ciència i Tecnologia Quàntiques / Quantum Science and Technology, Facultat de Física, Universitat de Barcelona. Curs: 2024-2025. Tutors: Timothy Heightman, Edward JiangLearning open quantum many-body dynamics is challenging: full Liouvillian models grow exponentially with system size, and dissipation and dephasing force us to follow mixed states from noisy, limited data. These factors make routine characterisation and control difficult, so we need methods that are data-efficient, scalable, and easy to interpret. We present an interpretable, robust framework for learning Lindbladian dynamics from minimal, hardwarefriendly data. The method pairs a physics-first CPTP Lindblad model with a small Neural Differential Equation (NDE) residual and uses a two-stage curriculum (neural warm-up, then analytic-only refinement) to reliably recover coherent and dissipative parameters on challenging 1D benchmarks. There are two ways in which robustness emerges in Lindladian learning: modest physical dissipation that smoothens loss landscapes via steady-state attraction, and the NDE residual that resolves remaining nonconvexity when paired with an optimizer reset. A transient infidelity metric shows short-time power-law error and small steady-state plateaus. Extending beyond CPTP to a stochastic dissipative qubit shows failures in noise-induced or deep PT-unbroken phases that are information-limited, not optimization-limitedHeightman, TimothyJiang, Edward2025info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2445/223226Màster Oficial - Ciència i Tecnologia Quàntiques / Quantum Science and Technologyreponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaIngléscc-by-nc-nd (c) Aseguinolaza, 2025http://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/2232262026-05-27T06:46:51Z
dc.title.none.fl_str_mv Solving the Quantum Many-Body Lindbladian Learning Problem With Neural Differential Equations
title Solving the Quantum Many-Body Lindbladian Learning Problem With Neural Differential Equations
spellingShingle Solving the Quantum Many-Body Lindbladian Learning Problem With Neural Differential Equations
Aseguinolaza Gallo, Roman
Equacions diferencials ordinàries
Aprenentatge automàtic
Treballs de fi de màster
Ordinary differential equations
Machine learning
Master's thesis
title_short Solving the Quantum Many-Body Lindbladian Learning Problem With Neural Differential Equations
title_full Solving the Quantum Many-Body Lindbladian Learning Problem With Neural Differential Equations
title_fullStr Solving the Quantum Many-Body Lindbladian Learning Problem With Neural Differential Equations
title_full_unstemmed Solving the Quantum Many-Body Lindbladian Learning Problem With Neural Differential Equations
title_sort Solving the Quantum Many-Body Lindbladian Learning Problem With Neural Differential Equations
dc.creator.none.fl_str_mv Aseguinolaza Gallo, Roman
author Aseguinolaza Gallo, Roman
author_facet Aseguinolaza Gallo, Roman
author_role author
dc.contributor.none.fl_str_mv Heightman, Timothy
Jiang, Edward
dc.subject.none.fl_str_mv Equacions diferencials ordinàries
Aprenentatge automàtic
Treballs de fi de màster
Ordinary differential equations
Machine learning
Master's thesis
topic Equacions diferencials ordinàries
Aprenentatge automàtic
Treballs de fi de màster
Ordinary differential equations
Machine learning
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: Timothy Heightman, Edward Jiang
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/2445/223226
url https://hdl.handle.net/2445/223226
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv cc-by-nc-nd (c) Aseguinolaza, 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) Aseguinolaza, 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
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score 15,812429

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