Machine learning en metrología cuántica
Este trabajo explora la aplicación de métodos de aprendizaje automático en la metrología cuántica, integrando técnicas clásicas y avanzadas para superar los límites impuestos por la mecánica cuántica en la medición. Se revisan fundamentos teóricos y prácticos de modelos como la regresión lineal, red...
- Autores:
-
Sánchez Vásquez, Emmanuel
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2025
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- spa
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/45717
- Acceso en línea:
- https://hdl.handle.net/10495/45717
- Palabra clave:
- Aprendizaje automático
Machine learning
Sistemas cuánticos
Quantum systems
Ordenadores cuánticos
Quantum computers
Mecánica cuántica
Quantum theory
Superficies de energía potencial
Potential energy surfaces
Funciones de Kernel
Kernel functions
Redes neurales (Computadores)
Neural networks (Computer science)
Mediciones cuánticas
http://id.loc.gov/authorities/subjects/sh85079324
http://id.loc.gov/authorities/subjects/sh2013002642
http://id.loc.gov/authorities/subjects/sh98002795
http://id.loc.gov/authorities/subjects/sh85109469
http://id.loc.gov/authorities/subjects/sh91003022
http://id.loc.gov/authorities/subjects/sh85072061
http://id.loc.gov/authorities/subjects/sh90001937
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-sa/4.0/
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Machine learning en metrología cuántica |
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Machine learning en metrología cuántica |
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Machine learning en metrología cuántica Aprendizaje automático Machine learning Sistemas cuánticos Quantum systems Ordenadores cuánticos Quantum computers Mecánica cuántica Quantum theory Superficies de energía potencial Potential energy surfaces Funciones de Kernel Kernel functions Redes neurales (Computadores) Neural networks (Computer science) Mediciones cuánticas http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh2013002642 http://id.loc.gov/authorities/subjects/sh98002795 http://id.loc.gov/authorities/subjects/sh85109469 http://id.loc.gov/authorities/subjects/sh91003022 http://id.loc.gov/authorities/subjects/sh85072061 http://id.loc.gov/authorities/subjects/sh90001937 |
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Machine learning en metrología cuántica |
| title_full |
Machine learning en metrología cuántica |
| title_fullStr |
Machine learning en metrología cuántica |
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Machine learning en metrología cuántica |
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Machine learning en metrología cuántica |
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Sánchez Vásquez, Emmanuel |
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Mahecha Gómez, Jorge Eduardo |
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Sánchez Vásquez, Emmanuel |
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Grupo de Física Atómica y Molecular |
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Aprendizaje automático Machine learning Sistemas cuánticos Quantum systems Ordenadores cuánticos Quantum computers Mecánica cuántica Quantum theory Superficies de energía potencial Potential energy surfaces Funciones de Kernel Kernel functions Redes neurales (Computadores) Neural networks (Computer science) |
| topic |
Aprendizaje automático Machine learning Sistemas cuánticos Quantum systems Ordenadores cuánticos Quantum computers Mecánica cuántica Quantum theory Superficies de energía potencial Potential energy surfaces Funciones de Kernel Kernel functions Redes neurales (Computadores) Neural networks (Computer science) Mediciones cuánticas http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh2013002642 http://id.loc.gov/authorities/subjects/sh98002795 http://id.loc.gov/authorities/subjects/sh85109469 http://id.loc.gov/authorities/subjects/sh91003022 http://id.loc.gov/authorities/subjects/sh85072061 http://id.loc.gov/authorities/subjects/sh90001937 |
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Mediciones cuánticas |
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http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh2013002642 http://id.loc.gov/authorities/subjects/sh98002795 http://id.loc.gov/authorities/subjects/sh85109469 http://id.loc.gov/authorities/subjects/sh91003022 http://id.loc.gov/authorities/subjects/sh85072061 http://id.loc.gov/authorities/subjects/sh90001937 |
| description |
Este trabajo explora la aplicación de métodos de aprendizaje automático en la metrología cuántica, integrando técnicas clásicas y avanzadas para superar los límites impuestos por la mecánica cuántica en la medición. Se revisan fundamentos teóricos y prácticos de modelos como la regresión lineal, redes neuronales, métodos de kernel y procesos gaussianos, y se analizan sus aplicaciones en sistemas atómicos y moleculares. En particular, se abordan problemas como el cambio de fase en el modelo de Ising y la interpolación de superficies de energía potencial en la molécula H3O+, demostrando que la integración de ML con conceptos cuánticos puede mejorar la precisión y robustez de las mediciones. Los resultados presentados ofrecen perspectivas y herramientas para la interpretación de datos en física y química modernas. |
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2025-04-21T15:15:55Z |
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2025 |
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Trabajo de grado - Pregrado |
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Sánchez Vásquez, E. (2025). Machine learning en Metrología cuántica. [Trabajo de grado profesional]. Universidad de Antioquia, Medellín, Colombia. |
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Sánchez Vásquez, E. (2025). Machine learning en Metrología cuántica. [Trabajo de grado profesional]. Universidad de Antioquia, Medellín, Colombia. |
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Caves, C. M.; Thorne, K. S.; Drever, R. W. P.; Sandberg, V. D. y Zimmermann, M. Rev. Mod. Phys. 1980, 52. Hentschel, A. y Sanders, B. C. Phys. Rev. Lett. 2010, 104. Pfau, D.; Spencer, J. S.; Matthews, A. G. D. G. y Foulkes, C. Phys. Rev. Res. 2020, 2. Rao, C. R. University of California Press 1972, 601-620. Goodfellow, I.; Bengio, Y. y Courville, A., Deep Learning; The MIT Press: 2016. Tibshirani, R. J. R. Stat. Soc. Ser. B Methodol. 1996, 58, 267. Zhou, Z.; Li, X. y Zare, R. N. ACS Cent. Sci 2017, 3, 1337. Kelleher, J. D.; Namee, B. M. y D’Arcy, A., Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies; The MIT Press: Cambridge, Massachusetts, 2020. Cybenko, G. Continuous valued neural networks with two hidden layers are sufficient; inf. t´ec.; Department of Computer Science, Tufts University, 1988. Reed, R. D. y Marks, R. J., Neural Smithing: Supervised Learning in Feedforward Artificial Networks; MIT Press: Cambridge, MA, 1999. Rosenblatt, F. Psychological Review 1958, 65, 386-408. Kelleher, J. D., Deep Learning; Essential Knowledge Series; MIT Press: Cambridge, MA, 2019. Minsky, M. y Papert, S. A., Perceptrons: An Introduction to Computational Geometry, Expanded Edition, Expanded, Subsequent; MIT Press: Cambridge, MA, 1987. Hecht-Nielsen, R. en Proceedings of the IEEE First International Conference on Neural Networks, 1987; vol. 3, p´ags. 11-13. Hornik, K.; Stinchcombe, M. y White, H. Neural Networks 1989, 2, 359-366. Cybenko, G. Mathematics of Control, Signals and Systems 1989, 2, 303-314. Leshno, M.; Lin, V. Y.; Pinkus, A. y Schocken, S. Neural Networks 1993, 6, 861-867. Goodfellow, I.; Bengio, Y. y Courville, A., Deep Learning; MIT Press: Cambridge, MA, 2016. Minsky, M. y Papert, S., Perceptrons; MIT Press: 1969. Fefferman, C.; Mitter, S. y Narayanan, H. J. Am. Math. Soc. 2016, 29, 983. Mehta, P.; Bukov, M.; Wang, C.-H.; Day, A. G.; Richardson, C.; Fisher, C. K. y Schwab, D. J. Phys. Rep. 2019, 810, 1. Dawid, A. et al. arXiv preprint arXiv:2204.04198 2022. Aronszajn, N. Transactions of the American Mathematical Society 1950, 68, 337-404. Schölkopf, B.; Herbrich, R. y Smola, A. J. en Computational Learning Theory; Springer: 2001, págs. 416-426. Bishop, C. M., Pattern Recognition and Machine Learning; Springer: Berlin, Heidelberg, 2006. Chervonenkis, A. en Support Vector Machines: Evolution and Applications; Springer Berlin Heidelberg: Berlin, Heidelberg, 2013, págs. 13-20. Boser, B. E.; Guyon, I. M. y Vapnik, V. N. en Proc. Fifth Ann. Workshop Compu. Learn. Theo., COLT’92, 1992, págs. 144-152. Platt, J. Sequential minimal optimization: A fast algorithm for training support vector machines; inf. téc. MSR-TR-98-14; Microsoft, 1998. Yang, C. N. Physical Review 1952, 85, 808-816. Wang, L. Phys. Rev. B 2016, 94, 195105. Carrasquilla, J. y Melko, R. G. Nat. Phys. 2017, 13, 431. Summer School: Machine Learning in Science and Technology GitHub repository with selected tutorials from the school, Accessed: 2024-09-15, 2021. Dai, J. y Krems, R. V. Journal of Chemical Theory and Computation 2020, 16, 1386-1395. Duvenaud, D. K.; Lloyd, J.; Grosse, R.; Tenenbaum, J. B. y Ghahramani, Z. en Proceedings of the 30th International Conference on Machine Learning (ICML), 2013; vol. 28, págs. 1166-1174. Duvenaud, D. K.; Nickisch, H. y Rasmussen, C. E. en Advances in Neural Information Processing Systems, 2011; vol. 24, p´ags. 226-234. Vargas-Hernandez, R.; Sous, J.; Berciu, M. y Krems, R. V. Physical Review Letters 2018, 121, 255702. Rasmussen, C. E. y Williams, C. K., Gaussian Processes for Machine Learning; MIT Press: Cambridge, MA, 2006. Schwarz, G. The Annals of Statistics 1978, 6, 461. Yu, Q. y Bowman, J. M. Journal of Chemical Theory and Computation 2016, 12, 5284-5292. Qu, C.; Yu, Q.; Van Hoozen Jr, B. L.; Bowman, J. M. y Vargas-Hernandez, R. A. Journal of Chemical Theory and Computation 2018, 14, 3381. Duvenaud, D.; Lloyd, J. R.; Grosse, R.; Tenenbaum, J. B. y Ghahramani, Z. en Proceedings of the 30th International Conference on Machine Learning, PMLR: 2013, págs. 1166-1174. Rosenblatt, F. Psychological Review 1958, 65, 386-408. |
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Mahecha Gómez, Jorge EduardoSánchez Vásquez, EmmanuelGrupo de Física Atómica y Molecular2025-04-21T15:15:55Z2025Sánchez Vásquez, E. (2025). Machine learning en Metrología cuántica. [Trabajo de grado profesional]. Universidad de Antioquia, Medellín, Colombia.https://hdl.handle.net/10495/45717Este trabajo explora la aplicación de métodos de aprendizaje automático en la metrología cuántica, integrando técnicas clásicas y avanzadas para superar los límites impuestos por la mecánica cuántica en la medición. Se revisan fundamentos teóricos y prácticos de modelos como la regresión lineal, redes neuronales, métodos de kernel y procesos gaussianos, y se analizan sus aplicaciones en sistemas atómicos y moleculares. En particular, se abordan problemas como el cambio de fase en el modelo de Ising y la interpolación de superficies de energía potencial en la molécula H3O+, demostrando que la integración de ML con conceptos cuánticos puede mejorar la precisión y robustez de las mediciones. Los resultados presentados ofrecen perspectivas y herramientas para la interpretación de datos en física y química modernas.Ciencias Exactas y NaturalesCOL0008441PregradoFísico129 páginasapplication/pdfspaUniversidad de AntioquiaFísicaInstituto de FísicaMedellín, ColombiaFacultad de Ciencias Exactas y NaturalesCampus Medellín - Ciudad Universitariahttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://purl.org/coar/access_right/c_abf2Aprendizaje automáticoMachine learningSistemas cuánticosQuantum systemsOrdenadores cuánticosQuantum computersMecánica cuánticaQuantum theorySuperficies de energía potencialPotential energy surfacesFunciones de KernelKernel functionsRedes neurales (Computadores)Neural networks (Computer science)Mediciones cuánticashttp://id.loc.gov/authorities/subjects/sh85079324http://id.loc.gov/authorities/subjects/sh2013002642http://id.loc.gov/authorities/subjects/sh98002795http://id.loc.gov/authorities/subjects/sh85109469http://id.loc.gov/authorities/subjects/sh91003022http://id.loc.gov/authorities/subjects/sh85072061http://id.loc.gov/authorities/subjects/sh90001937Machine learning en metrología cuánticaTrabajo de grado - Pregradohttp://purl.org/coar/resource_type/c_7a1fhttp://purl.org/redcol/resource_type/TPTexthttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/draftCaves, C. M.; Thorne, K. S.; Drever, R. W. P.; Sandberg, V. D. y Zimmermann, M. Rev. Mod. Phys. 1980, 52.Hentschel, A. y Sanders, B. C. Phys. Rev. Lett. 2010, 104.Pfau, D.; Spencer, J. S.; Matthews, A. G. D. G. y Foulkes, C. Phys. Rev. Res. 2020, 2.Rao, C. R. University of California Press 1972, 601-620.Goodfellow, I.; Bengio, Y. y Courville, A., Deep Learning; The MIT Press: 2016.Tibshirani, R. J. R. Stat. Soc. Ser. B Methodol. 1996, 58, 267.Zhou, Z.; Li, X. y Zare, R. N. ACS Cent. Sci 2017, 3, 1337.Kelleher, J. D.; Namee, B. M. y D’Arcy, A., Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies; The MIT Press: Cambridge, Massachusetts, 2020.Cybenko, G. Continuous valued neural networks with two hidden layers are sufficient; inf. t´ec.; Department of Computer Science, Tufts University, 1988.Reed, R. D. y Marks, R. J., Neural Smithing: Supervised Learning in Feedforward Artificial Networks; MIT Press: Cambridge, MA, 1999.Rosenblatt, F. Psychological Review 1958, 65, 386-408.Kelleher, J. D., Deep Learning; Essential Knowledge Series; MIT Press: Cambridge, MA, 2019.Minsky, M. y Papert, S. A., Perceptrons: An Introduction to Computational Geometry, Expanded Edition, Expanded, Subsequent; MIT Press: Cambridge, MA, 1987.Hecht-Nielsen, R. en Proceedings of the IEEE First International Conference on Neural Networks, 1987; vol. 3, p´ags. 11-13.Hornik, K.; Stinchcombe, M. y White, H. Neural Networks 1989, 2, 359-366.Cybenko, G. Mathematics of Control, Signals and Systems 1989, 2, 303-314.Leshno, M.; Lin, V. Y.; Pinkus, A. y Schocken, S. Neural Networks 1993, 6, 861-867.Goodfellow, I.; Bengio, Y. y Courville, A., Deep Learning; MIT Press: Cambridge, MA, 2016.Minsky, M. y Papert, S., Perceptrons; MIT Press: 1969.Fefferman, C.; Mitter, S. y Narayanan, H. J. Am. Math. Soc. 2016, 29, 983.Mehta, P.; Bukov, M.; Wang, C.-H.; Day, A. G.; Richardson, C.; Fisher, C. K. y Schwab, D. J. Phys. Rep. 2019, 810, 1.Dawid, A. et al. arXiv preprint arXiv:2204.04198 2022.Aronszajn, N. Transactions of the American Mathematical Society 1950, 68, 337-404.Schölkopf, B.; Herbrich, R. y Smola, A. J. en Computational Learning Theory; Springer: 2001, págs. 416-426.Bishop, C. M., Pattern Recognition and Machine Learning; Springer: Berlin, Heidelberg, 2006.Chervonenkis, A. en Support Vector Machines: Evolution and Applications; Springer Berlin Heidelberg: Berlin, Heidelberg, 2013, págs. 13-20.Boser, B. E.; Guyon, I. M. y Vapnik, V. N. en Proc. Fifth Ann. Workshop Compu. Learn. Theo., COLT’92, 1992, págs. 144-152.Platt, J. Sequential minimal optimization: A fast algorithm for training support vector machines; inf. téc. MSR-TR-98-14; Microsoft, 1998.Yang, C. N. Physical Review 1952, 85, 808-816.Wang, L. Phys. Rev. B 2016, 94, 195105.Carrasquilla, J. y Melko, R. G. Nat. Phys. 2017, 13, 431.Summer School: Machine Learning in Science and Technology GitHub repository with selected tutorials from the school, Accessed: 2024-09-15, 2021.Dai, J. y Krems, R. V. Journal of Chemical Theory and Computation 2020, 16, 1386-1395.Duvenaud, D. K.; Lloyd, J.; Grosse, R.; Tenenbaum, J. B. y Ghahramani, Z. en Proceedings of the 30th International Conference on Machine Learning (ICML), 2013; vol. 28, págs. 1166-1174.Duvenaud, D. K.; Nickisch, H. y Rasmussen, C. E. en Advances in Neural Information Processing Systems, 2011; vol. 24, p´ags. 226-234.Vargas-Hernandez, R.; Sous, J.; Berciu, M. y Krems, R. V. Physical Review Letters 2018, 121, 255702.Rasmussen, C. E. y Williams, C. K., Gaussian Processes for Machine Learning; MIT Press: Cambridge, MA, 2006. Schwarz, G. The Annals of Statistics 1978, 6, 461.Yu, Q. y Bowman, J. M. Journal of Chemical Theory and Computation 2016, 12, 5284-5292.Qu, C.; Yu, Q.; Van Hoozen Jr, B. L.; Bowman, J. M. y Vargas-Hernandez, R. A. Journal of Chemical Theory and Computation 2018, 14, 3381.Duvenaud, D.; Lloyd, J. R.; Grosse, R.; Tenenbaum, J. B. y Ghahramani, Z. en Proceedings of the 30th International Conference on Machine Learning, PMLR: 2013, págs. 1166-1174.Rosenblatt, F. 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