Quantum generative adversarial networks for high energy physics

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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.authorDíaz Lievano, Lázaro Raúl
dc.date.accessioned2025-06-17T19:32:04Z
dc.date.available2025-06-17T19:32:04Z
dc.date.issued2025-04
dc.description.abstract"The increasing computational demands of the High-Luminosity Large Hadron Collider (HL-LHC) have made quantum computing a promising tool for advancing high-energy physics (HEP) research. This thesis explores the application of Quantum Generative Adversarial Networks (QGANs) to address two central challenges in HEP: particle identification and data generation. Specifically, a dual-task QGAN model was developed using simulated data from Delphes, aiming both to distinguish real from generated events and to classify jets as either signal or background. The model integrates quantum generators and discriminators through TensorFlow Quantum and Google Cirq, thus enabling a hybrid architecture that combines quantum circuits with classical neural networks. As a result, the system achieved a classification accuracy of 90%, showing promising capabilities in mimicking real data distributions while simultaneously performing classification. However, it is important to note that this work does not claim quantum advantage, as the technology is still at an early stage. Nevertheless, the study demonstrates the feasibility of applying QGANs to computationally demanding HEP problems and suggests that, with further exploration into circuit design, data complexity, and scalability, quantum computing could serve as a complementary method to classical approaches in the HL-LHC era".
dc.folio20250402101800-5901-TL
dc.formatpdf
dc.identificator1
dc.identifier.urihttps://hdl.handle.net/20.500.12371/28893
dc.language.isoeng
dc.matricula.creator201910145
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 atómica--Teoría cuántica
dc.subject.lccAprendizaje automático (Inteligencia artificial)
dc.subject.lccComputación cuántica--Investigación
dc.subject.lccPartículas (Física nuclear)--Procesamiento de datos
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 high energy physics
dc.typeTesis de licenciatura
dc.type.conacytbachelorThesis
dc.type.degreeLicenciatura
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