Quantum generative adversarial networks for data augmentation

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dc.audiencegeneralPublic
dc.contributorPedraza Morales, María Isabel
dc.contributorVarela Carlos, Enrique
dc.contributor.advisorPedraza Morales, María Isabel; 0000-0002-2669-4659
dc.contributor.advisorVarela Carlos, Enrique; 0000-0003-0715-7513
dc.contributor.authorTorres Tapia, Joshua Devin
dc.date.accessioned2026-07-08T19:36:26Z
dc.date.available2026-07-08T19:36:26Z
dc.date.issued2025-12-12
dc.description.abstract"In this thesis, we address a high-energy physics problem involving two types of events, signal and background, with 50 samples for each where signal events are gluons and background events quarks. Each sample has five features that define the type of event. A quantum generative adversarial network model is proposed, which is trained separately on each type of data. The goal is to replicate the features of each sample, thereby expanding the dataset where sample scarcity was a problem. For this purpose, we propose two very similar codes; the difference between them is that in one, only the samples classified as background are loaded, while in the other, only those classified as signal are loaded, the rest of the architecture is the same. The model is expected to successfully generate samples that imitate the features. If the model is capable of such a thing, then this means it is indeed possible to train a quantum generative adversarial networks model with a small amount of samples to generate realistic data. Another thing that is expected to be proven or refuted with this thesis is that, for this task, a pure quantum model (quantum generator and discriminator) is better than a hybrid one (quantum generator and classic discriminator). With this approach, we aim to demonstrate the usefulness of a quantum architecture in problems where data augmentation is required due to the limited amount of available data".
dc.folio20260119130517-1931-TL
dc.formatpdf
dc.identificator1
dc.identifier.urihttps://hdl.handle.net/20.500.12371/33369
dc.language.isoeng
dc.matricula.creator201845298
dc.publisherBenemérita Universidad Autónoma de Puebla
dc.rights.accesopenAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subject.classificationCIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA
dc.subject.lccFísica--Física nuclear y de partículas--Física de partículas elementales--Temas especiales--Física de altas energías
dc.subject.lccComputación cuántica
dc.subject.lccPartículas (Física nuclear)--Procesamiento de datos
dc.subject.lccRedes neuronales (Computación)
dc.subject.lccAprendizaje automático (Inteligencia artificial)
dc.thesis.careerLicenciatura en Física
dc.thesis.degreedisciplineÁrea de Ingeniería y Ciencias Exactas
dc.thesis.degreegrantorFacultad de Ciencias Físico Matemáticas
dc.thesis.degreetoobtainLicenciado (a) en Física
dc.titleQuantum generative adversarial networks for data augmentation
dc.typeTesis de licenciatura
dc.type.conacytbachelorThesis
dc.type.degreeLicenciatura
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