Pulse-level characterization of qubits on quantum devices
ABSTRACT: Recent research has tackled the problem of mitigating noise present in quantum computers in the Noisy Intermediate-Scale Quantum (NISQ) era to enable precise computations and to benefit from the intrinsic properties of quantum mechanics. For this matter, an important task is the characteri...
- Autores:
-
Quiroga Salamanca, David Andrés
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2021
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- eng
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/22001
- Acceso en línea:
- http://hdl.handle.net/10495/22001
- Palabra clave:
- Quantum theory
Teoría cuántica
Algorithms
Algoritmo
Calibration
Calibración
Crosstalk
Machine Learning
Quantum Benchmarks
Quantum Computing
Quantum Machine Learning
Quantum Optimal Control
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http://vocabularies.unesco.org/thesaurus/concept2024
http://vocabularies.unesco.org/thesaurus/concept4530
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by/2.5/co/
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dc.title.spa.fl_str_mv |
Pulse-level characterization of qubits on quantum devices |
title |
Pulse-level characterization of qubits on quantum devices |
spellingShingle |
Pulse-level characterization of qubits on quantum devices Quantum theory Teoría cuántica Algorithms Algoritmo Calibration Calibración Crosstalk Machine Learning Quantum Benchmarks Quantum Computing Quantum Machine Learning Quantum Optimal Control http://vocabularies.unesco.org/thesaurus/concept4810 http://vocabularies.unesco.org/thesaurus/concept2024 http://vocabularies.unesco.org/thesaurus/concept4530 |
title_short |
Pulse-level characterization of qubits on quantum devices |
title_full |
Pulse-level characterization of qubits on quantum devices |
title_fullStr |
Pulse-level characterization of qubits on quantum devices |
title_full_unstemmed |
Pulse-level characterization of qubits on quantum devices |
title_sort |
Pulse-level characterization of qubits on quantum devices |
dc.creator.fl_str_mv |
Quiroga Salamanca, David Andrés |
dc.contributor.advisor.none.fl_str_mv |
Rivera Vélez, Fredy Alexander Pooser, Raphael C. |
dc.contributor.author.none.fl_str_mv |
Quiroga Salamanca, David Andrés |
dc.subject.unesco.none.fl_str_mv |
Quantum theory Teoría cuántica Algorithms Algoritmo Calibration Calibración |
topic |
Quantum theory Teoría cuántica Algorithms Algoritmo Calibration Calibración Crosstalk Machine Learning Quantum Benchmarks Quantum Computing Quantum Machine Learning Quantum Optimal Control http://vocabularies.unesco.org/thesaurus/concept4810 http://vocabularies.unesco.org/thesaurus/concept2024 http://vocabularies.unesco.org/thesaurus/concept4530 |
dc.subject.proposal.spa.fl_str_mv |
Crosstalk Machine Learning Quantum Benchmarks Quantum Computing Quantum Machine Learning Quantum Optimal Control |
dc.subject.unescouri.none.fl_str_mv |
http://vocabularies.unesco.org/thesaurus/concept4810 http://vocabularies.unesco.org/thesaurus/concept2024 http://vocabularies.unesco.org/thesaurus/concept4530 |
description |
ABSTRACT: Recent research has tackled the problem of mitigating noise present in quantum computers in the Noisy Intermediate-Scale Quantum (NISQ) era to enable precise computations and to benefit from the intrinsic properties of quantum mechanics. For this matter, an important task is the characterization of qubits available in quantum devices so as to provide insights on how to reduce noise on the final output of a quantum circuit. Characterization comprises analysis of noise sources and this information can be used to reduce noise with methods such as Cycle Benchmarking, Quantum Error Mitigation, Quantum Error Correction and others. Here, we study optimization of pulses through Quantum Optimal Control (QOC) to obtain higher gate fidelity. We will explore an algorithm that performs calibration on specific quantum gates by implementing optimized pulse schedules to subsequently use the algorithm for analysis of noise sources. Using calibrated gates as input, several benchmarking protocols, including pulse noise extrapolation, leakage analysis from quantum optimal control, and machine learning based classification of qubit readout, will be tested to extract precise information on how noise influences the analyzed qubits. We will explain and discuss different techniques for obtaining properties of qubits and quantum computers. We will implement state discrimination with a Machine Learning (ML) focus to analyze readout errors caused by factors such as cross-talk and leakage into higher quantum states. We will perform noise fitting of optimized pulses and evaluate the effectiveness of important quantum algorithms at the pulse level. |
publishDate |
2021 |
dc.date.accessioned.none.fl_str_mv |
2021-08-30T19:53:46Z |
dc.date.available.none.fl_str_mv |
2021-08-30T19:53:46Z |
dc.date.issued.none.fl_str_mv |
2021 |
dc.type.spa.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_b1a7d7d4d402bcce |
dc.type.hasversion.spa.fl_str_mv |
info:eu-repo/semantics/draft |
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http://purl.org/coar/resource_type/c_7a1f |
dc.type.redcol.spa.fl_str_mv |
https://purl.org/redcol/resource_type/TP |
dc.type.local.spa.fl_str_mv |
Tesis/Trabajo de grado - Monografía - Pregrado |
format |
http://purl.org/coar/resource_type/c_7a1f |
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draft |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10495/22001 |
url |
http://hdl.handle.net/10495/22001 |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.rights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by/2.5/co/ |
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openAccess |
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application/pdf |
dc.publisher.group.spa.fl_str_mv |
Sistemas Embebidos e Inteligencia Computacional (SISTEMIC) |
dc.publisher.place.spa.fl_str_mv |
Medellín |
institution |
Universidad de Antioquia |
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andres.perez@udea.edu.co |
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Rivera Vélez, Fredy AlexanderPooser, Raphael C.Quiroga Salamanca, David Andrés2021-08-30T19:53:46Z2021-08-30T19:53:46Z2021http://hdl.handle.net/10495/22001ABSTRACT: Recent research has tackled the problem of mitigating noise present in quantum computers in the Noisy Intermediate-Scale Quantum (NISQ) era to enable precise computations and to benefit from the intrinsic properties of quantum mechanics. For this matter, an important task is the characterization of qubits available in quantum devices so as to provide insights on how to reduce noise on the final output of a quantum circuit. Characterization comprises analysis of noise sources and this information can be used to reduce noise with methods such as Cycle Benchmarking, Quantum Error Mitigation, Quantum Error Correction and others. Here, we study optimization of pulses through Quantum Optimal Control (QOC) to obtain higher gate fidelity. We will explore an algorithm that performs calibration on specific quantum gates by implementing optimized pulse schedules to subsequently use the algorithm for analysis of noise sources. Using calibrated gates as input, several benchmarking protocols, including pulse noise extrapolation, leakage analysis from quantum optimal control, and machine learning based classification of qubit readout, will be tested to extract precise information on how noise influences the analyzed qubits. We will explain and discuss different techniques for obtaining properties of qubits and quantum computers. We will implement state discrimination with a Machine Learning (ML) focus to analyze readout errors caused by factors such as cross-talk and leakage into higher quantum states. We will perform noise fitting of optimized pulses and evaluate the effectiveness of important quantum algorithms at the pulse level.RESUMEN: Investigaciones recientes han abarcado el problema de mitigar ruido presente en computadores cuánticos en la era NISQ para permitir computaciones precisas y para encontrar ventajas en las propiedades intrínsecas de la mecánica cuántica. Para tal efecto, una tarea importante es la caracterización de qubits disponibles en computadores cuánticos para proveer información sobre cómo reducir ruido en la salida final de un circuito cuántico. La caracterización comprende el análisis de fuentes de ruido y esta información puede ser usada para reducir ruido con métodos como Cycle Benchmarking, Quantum Error Mitigation, Quantum Error Correction y otros. Aquí estudiamos optimización de pulsos a través de QOC para obtener fidelidades de compuerta más altas. Exploraremos un algoritmo que realiza calibración a compuertas cuánticas específicas implementando pulsos optimizados para consecuentemente utilizar el algoritmo para análisis de fuentes de ruido. Usando compuertas calibradas como entrada, varios protocolos de benchmarking incluyendo extrapolación de ruido, análisis de fuga con control cuántico óptimo y clasificación de datos de salida de qubits basada en machine learning serán probados para extraer información precisa de cómo el ruido influye los qubits analizados. Explicaremos y discutiremos diferentes técnicas para obtener propiedades de qubits y de computadores cuánticos. Implementaremos discriminación de estados con un enfoque en ML para alanizar errores de lectura causados por factores como charla cruzada y fuga hacia estados cuánticos más altos. Realizaremos ajuste del ruido de pulsos optimizados para evaluar la efectividad de algoritmos cuánticos importantes a nivel de pulsos.97application/pdfenginfo:eu-repo/semantics/draftinfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/resource_type/c_7a1fhttps://purl.org/redcol/resource_type/TPTesis/Trabajo de grado - Monografía - Pregradohttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/2.5/co/http://purl.org/coar/access_right/c_abf2https://creativecommons.org/licenses/by-nc-sa/4.0/Pulse-level characterization of qubits on quantum devicesSistemas Embebidos e Inteligencia Computacional (SISTEMIC)MedellínQuantum theoryTeoría cuánticaAlgorithmsAlgoritmoCalibrationCalibraciónCrosstalkMachine LearningQuantum BenchmarksQuantum ComputingQuantum Machine LearningQuantum Optimal Controlhttp://vocabularies.unesco.org/thesaurus/concept4810http://vocabularies.unesco.org/thesaurus/concept2024http://vocabularies.unesco.org/thesaurus/concept4530Profesional en Ingeniería de SistemasPregradoFacultad de Ingeniería. Carrera de Ingeniería de SistemasUniversidad de AntioquiaCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8927http://bibliotecadigital.udea.edu.co/bitstream/10495/22001/6/license_rdf1646d1f6b96dbbbc38035efc9239ac9cMD56ORIGINALDavidQuiroga_2021_PulseLevelCharacterization.pdfDavidQuiroga_2021_PulseLevelCharacterization.pdfTrabajo de grado de pregradoapplication/pdf1686919http://bibliotecadigital.udea.edu.co/bitstream/10495/22001/4/DavidQuiroga_2021_PulseLevelCharacterization.pdf07e04e0c16f68d376f2538d863c5b40bMD54LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://bibliotecadigital.udea.edu.co/bitstream/10495/22001/7/license.txt8a4605be74aa9ea9d79846c1fba20a33MD5710495/22001oai:bibliotecadigital.udea.edu.co:10495/220012021-08-30 14:54:35.908Repositorio Institucional Universidad de Antioquiaandres.perez@udea.edu.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 |