Ground state energies of H2 using variational quantum circuits

ilustraciones, diagramas

Autores:
Cotrino Sandoval, Sergio Andrés
Tipo de recurso:
Fecha de publicación:
2024
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
eng
OAI Identifier:
oai:repositorio.unal.edu.co:unal/86333
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/86333
https://repositorio.unal.edu.co/
Palabra clave:
530 - Física::539 - Física moderna
540 - Química y ciencias afines::541 - Química física
QUIMICA CUANTICA
Quantum chemistry
quantum circuits
Variational Quantum Eigensolver
quantum machine learning
VQE
circuitos cuánticos
autosolucionador cuántico variacional
aprendizaje automático cuántico
PennyLane
computación cuántica
quantum computing
Rights
openAccess
License
Atribución-CompartirIgual 4.0 Internacional
id UNACIONAL2_df7518024245515a14f4ac68b42dc1f2
oai_identifier_str oai:repositorio.unal.edu.co:unal/86333
network_acronym_str UNACIONAL2
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repository_id_str
dc.title.eng.fl_str_mv Ground state energies of H2 using variational quantum circuits
dc.title.translated.spa.fl_str_mv Energías de estado fundamental de H2 usando circuitos cuánticos variacionales
title Ground state energies of H2 using variational quantum circuits
spellingShingle Ground state energies of H2 using variational quantum circuits
530 - Física::539 - Física moderna
540 - Química y ciencias afines::541 - Química física
QUIMICA CUANTICA
Quantum chemistry
quantum circuits
Variational Quantum Eigensolver
quantum machine learning
VQE
circuitos cuánticos
autosolucionador cuántico variacional
aprendizaje automático cuántico
PennyLane
computación cuántica
quantum computing
title_short Ground state energies of H2 using variational quantum circuits
title_full Ground state energies of H2 using variational quantum circuits
title_fullStr Ground state energies of H2 using variational quantum circuits
title_full_unstemmed Ground state energies of H2 using variational quantum circuits
title_sort Ground state energies of H2 using variational quantum circuits
dc.creator.fl_str_mv Cotrino Sandoval, Sergio Andrés
dc.contributor.advisor.none.fl_str_mv Viviescas, Carlos
dc.contributor.author.none.fl_str_mv Cotrino Sandoval, Sergio Andrés
dc.contributor.researchgroup.spa.fl_str_mv Caos y Complejidad
dc.subject.ddc.spa.fl_str_mv 530 - Física::539 - Física moderna
540 - Química y ciencias afines::541 - Química física
topic 530 - Física::539 - Física moderna
540 - Química y ciencias afines::541 - Química física
QUIMICA CUANTICA
Quantum chemistry
quantum circuits
Variational Quantum Eigensolver
quantum machine learning
VQE
circuitos cuánticos
autosolucionador cuántico variacional
aprendizaje automático cuántico
PennyLane
computación cuántica
quantum computing
dc.subject.lemb.none.fl_str_mv QUIMICA CUANTICA
Quantum chemistry
dc.subject.proposal.eng.fl_str_mv quantum circuits
Variational Quantum Eigensolver
quantum machine learning
VQE
dc.subject.proposal.spa.fl_str_mv circuitos cuánticos
autosolucionador cuántico variacional
aprendizaje automático cuántico
dc.subject.proposal.none.fl_str_mv PennyLane
dc.subject.wikidata.none.fl_str_mv computación cuántica
quantum computing
description ilustraciones, diagramas
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-06-28T20:12:58Z
dc.date.available.none.fl_str_mv 2024-06-28T20:12:58Z
dc.date.issued.none.fl_str_mv 2024
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/86333
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/86333
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv eng
language eng
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dc.publisher.faculty.spa.fl_str_mv Facultad de Ciencias
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spelling Atribución-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Viviescas, Carlosf5a3bb5922f522614153d8dfb8996953600Cotrino Sandoval, Sergio Andrés076fe3731bcae44cb1f85dd619503b45Caos y Complejidad2024-06-28T20:12:58Z2024-06-28T20:12:58Z2024https://repositorio.unal.edu.co/handle/unal/86333Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasConsidering the current limitations on size and reliability of Noisy Intermediate Quantum Scale devices, Variational Quantum Circuits offer a way to get useful results from quantum computation. On top of that, Machine Learning methods using quantum data offer a way to process the information, but also use it to learn and extract useful information. Meta- Variational Quantum Eigensolver (meta-VQE) was used to learn the ground energy profile of a molecule using a set of training points. By training an ansatz circuit using a non-linear Gaussian encoding of each circuit parameter and setting the interatomic distance as a free parameter, it was possible to find a good approximation of the ground energy of the system for any interatomic distance within a certain region. This method also has the advantage to produce good starting parameters to train using standard VQE, and obtain even better results (opt-meta-VQE). Meta-VQE was implemented in an analytic noise-free simulation and a 10000 shots-based simulation using the software framework for quantum computing PennyLane. In the analytic simulation, it was possible to accurately describe the potential energy surface of an H2 molecule within chemical accuracy, using a hardware inspired ansatz and the ADAM optimizer. With the 10000 shots-based simulation, the method is capable to approximate the energy profile, but in general its performance is not as good as the analytical approach due to the variability on the samples obtained. Meta-VQE provides a novel way to extract and produce information by learning using quantum data from variational circuits.Teniendo en cuenta las limitaciones actuales de tamaño y confiabilidad de los dispositi- vos de escala cuántica intermedia ruidosa, los circuitos cuánticos variacionales ofrecen una forma de obtener resultados útiles de la computación cuántica. Además de eso, los méto- dos de aprendizaje automático que utilizan datos cuánticos ofrecen una forma de procesar la información, pero también de usarla para aprender y extraer información útil. Se usó el metodo de meta-autosolucionador cuántico variacional (meta-VQE, por sus siglas en inglés) para aprender el perfil de energı́a fundamental de una molécula usando un conjunto de pun- tos de entrenamiento. Al entrenar un circuito usando una codificación gaussiana no lineal de cada parámetro del circuito y estableciendo la distancia interatómica como un paráme- tro libre, fue posible encontrar una buena aproximación de la energı́a mı́nima del sistema para cualquier distancia interatómica dentro de una región determinada. Este método tam- bién tiene la ventaja de producir buenos parámetros de partida para entrenar usando VQE estándar y obtener resultados aún mejores (opt-meta-VQE). Meta-VQE se implementó en una simulación analı́tica sin ruido y una simulación basada en 10000 muestras utilizando el software para computación cuántica PennyLane. En la simulación analı́tica, fue posible describir con precisión la superficie de energı́a potencial de una molécula H2 con precisión quı́mica, utilizando un ansatz inspirado en hardware y el optimizador ADAM. Con la si- mulación basada en 10000 muestras, el método es capaz de aproximar el perfil de energı́a, pero en general no funciona tan bien como el enfoque analı́tico debido a la variabilidad de las muestras obtenidas. Meta-VQE proporciona una forma novedosa de extraer y producir información mediante el aprendizaje utilizando datos cuánticos de circuitos variacionales.MaestríaMagíster en Ciencias - FísicaQuantum Computingxv, 71 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - FísicaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá530 - Física::539 - Física moderna540 - Química y ciencias afines::541 - Química físicaQUIMICA CUANTICAQuantum chemistryquantum circuitsVariational Quantum Eigensolverquantum machine learningVQEcircuitos cuánticosautosolucionador cuántico variacionalaprendizaje automático cuánticoPennyLanecomputación cuánticaquantum computingGround state energies of H2 using variational quantum circuitsEnergías de estado fundamental de H2 usando circuitos cuánticos variacionalesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMThe quantum state of affairs. 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An empirical comparison of optimizers for quantum machine learning with spsa-based gradients. arXiv preprint arXiv:2305.00224, 2023.Público generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86333/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINALscotrino_master_thesis_june_2024.pdfscotrino_master_thesis_june_2024.pdfTesis de Maestría en Ciencias -Físicaapplication/pdf1361573https://repositorio.unal.edu.co/bitstream/unal/86333/4/scotrino_master_thesis_june_2024.pdf9c08168c3b4853e5658ff3f50779b8c0MD54THUMBNAILscotrino_master_thesis_june_2024.pdf.jpgscotrino_master_thesis_june_2024.pdf.jpgGenerated Thumbnailimage/jpeg4272https://repositorio.unal.edu.co/bitstream/unal/86333/5/scotrino_master_thesis_june_2024.pdf.jpg16c604de8b1c935271aa46a98dc1d8b8MD55unal/86333oai:repositorio.unal.edu.co:unal/863332024-06-28 23:05:22.266Repositorio Institucional Universidad Nacional de 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