Cuántica e inteligencia artificial: explorando su convergencia desde la cienciometría

Autores/as

  • Carlos Alberto Oviedo Machado Universidad Nacional de Colombia
  • Luiyi David Brito Palmezano Universidad Nacional de Colombia
  • William Alcides Trillos Sarmiento Universidad Nacional de Colombia
  • Tatiana Isabel Causil-Contreras Universidad Nacional de Colombia

DOI:

https://doi.org/10.18041/2619-4465/interfaces.1.13396

Palabras clave:

Inteligencia Artificial, Cuántica, Cienciometría, Convergencia tecnológica, Análisis de redes

Resumen

Los avances tecnológicos y científicos de los últimos años nos han permitido manipular tecnologías que antes se consideraban inimaginables. Entre ellas, la Inteligencia Artificial (IA) y la física cuántica han surgido como fuerzas fundamentales, mejorando significativamente nuestra capacidad para alcanzar objetivos y resolver problemas complejos. Dada su creciente relevancia, una revisión exhaustiva de la producción científica que sustenta esta nueva era es oportuna y necesaria. Tal revisión permite rastrear el creciente interés académico en estos dominios e identificar tendencias emergentes en la sinergia entre ellos. En consecuencia, el objetivo de este artículo es presentar un análisis cienciométrico de la literatura académica relacionada tanto con la IA como con la ciencia cuántica. Para ello, se examinaron artículos revisados por pares de bases de datos de prestigio como Scopus y Web of Science. El estudio abarca el periodo comprendido entre 2004 y 2024, analizando métricas clave como el crecimiento de las publicaciones, los autores más influyentes, las instituciones líderes y la distribución geográfica de la investigación. Los resultados revelan una trayectoria de crecimiento exponencial, con un marcado aumento de las publicaciones interdisciplinarias en la última década. Este análisis proporciona una visión estructurada de la intersección entre la computación cuántica y la IA, ofreciendo una base sólida para que investigadores y profesionales identifiquen áreas prioritarias para la innovación futura.

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Referencias

[1]F. F. Flöther et al., “How quantum computing can enhance biomarker discovery,” Patterns (N. Y.), vol. 6, no. 6, p. 101236, April. 2025, doi: 10.1016/j.patter.2025.101236. Available: https://doi.org/10.1016/j.patter.2025.101236

[2]S. S. Singh, S. Kumar, R. Ahuja, and J. Barua, “Fusion of quantum computing and explainable AI: A comprehensive survey on transformative healthcare solutions,” Inf. Fusion, vol. 122, no. 103217, p. 103217, Oct. 2025, doi: 10.1016/j.inffus.2025.103217. Available: https://doi.org/10.1016/j.inffus.2025.103217

[3]A. Agnihotri and S. Bhattacharya, “Chatbots’ effectiveness in service recovery,” Int. J. Inf. Manage., vol. 76, no. 102679, p. 102679, Jun. 2024, doi: 10.1016/j.ijinfomgt.2023.102679. Available: https://doi.org/10.1016/j.ijinfomgt.2023.102679

[4]R. Ramya, P. Kumar, D. Dhanasekaran, R. S. Kumar, and S. A. Sharavan, “A review of quantum communication and information networks with advanced cryptographic applications using machine learning, deep learning techniques,” Franklin Open, vol. 10, no. 100223, p. 100223, Mar. 2025, doi: 10.1016/j.fraope.2025.100223. Available: https://doi.org/10.1016/j.fraope.2025.100223

[5]P. A. Erdman et al., “Artificially intelligent Maxwell’s demon for optimal control of open quantum systems,” Quantum Sci. Technol., vol. 10, no. 2, p. 025047, Apr. 2025, doi: 10.1088/2058-9565/adbccf. Available: http://dx.doi.org/10.1088/2058-9565/adbccf

[6]C. Malica et al., “Artificial intelligence for advanced functional materials: exploring current and future directions,” JPhys Mater., vol. 8, no. 2, p. 021001, Apr. 2025, doi: 10.1088/2515-7639/adc29d. Available: http://dx.doi.org/10.1088/2515-7639/adc29d

[7]T. Pook et al., “Assessing the potential of quantum computing in agriculture,” Comput. Electron. Agric., vol. 235, no. 110332, p. 110332, Aug. 2025, doi: 10.1016/j.compag.2025.110332. Available: http://dx.doi.org/10.1016/j.compag.2025.110332

[8]V. Kumar, A. R. Ashraf, and W. Nadeem, “AI-powered marketing: What, where, and how?,” Int. J. Inf. Manage., vol. 77, no. 102783, p. 102783, Aug. 2024, doi: 10.1016/j.ijinfomgt.2024.102783. Available: http://dx.doi.org/10.1016/j.ijinfomgt.2024.102783

[9]S. D. M. Oñate and A. F. T. Herazo, “Agrivoltaic systems: a contribution to sustainability,” interfaces, vol. 7, no. 2, 2024, Available: https://revistas.unilibre.edu.co/index.php/interfaces/article/view/12713. [Accessed: Jun. 24, 2025]

[10]K. M. Romero Villareal and M. C. M. Murgas, “Antimicrobial Potential of Secondary Metabolites: AScientometric Review,” interfaces, vol. 7, no. 2, 2024, Available: https://revistas.unilibre.edu.co/index.php/interfaces/article/view/12712. [Accessed: Jan. 24, 2025]

[11]A. J. B. Berrocal and D. M. C. Rizo, “Scientometric Analysis of the Relationship Between Artificial Intelligence and Data Engineering: Trends,Collaboration, and Evolution,” interfaces, vol. 7, no. 2, 2024, Available: https://revistas.unilibre.edu.co/index.php/interfaces/article/view/12714. [Accessed: Jan. 24, 2025]

[12]S. Valencia, M. Zuluaga, M. C. Florian Pérez, K. F. Montoya-Quintero, M. S. Candamil-Cortés, and S. Robledo, “Human Gut Microbiome: A Connecting Organ Between Nutrition, Metabolism, and Health,” Int J Mol Sci, vol. 26, no. 9, Apr. 2025, doi: 10.3390/ijms26094112. Available: http://dx.doi.org/10.3390/ijms26094112

[13]S. Robledo, B. Arias, C. García, I. Durley-Torres, and M. Zuluaga, “Margaret: Streamlining research productivity analysis in Colombia with an R package for GrupLAC integration,” Issu. Sci. Technol. Libr.., no. 108, Nov. 2024, doi: 10.29173/istl2777. Available: http://dx.doi.org/10.29173/istl2777

[14]S. Robledo, L. Valencia, M. Zuluaga, O. A. Echeverri, and J. W. A. Valencia, “tosr: Create the Tree of Science from WoS and Scopus,” J. Sci. Res., vol. 13, no. 2, pp. 459–465, Aug. 2024, doi: 10.5530/jscires.13.2.36. Available: http://dx.doi.org/10.5530/jscires.13.2.36

[15]V. Dunjko and H. J. Briegel, “Machine learning & artificial intelligence in the quantum domain: a review of recent progress,” Rep. Prog. Phys., vol. 81, no. 7, p. 074001, Jul. 2018, doi: 10.1088/1361-6633/aab406. Available: http://dx.doi.org/10.1088/1361-6633/aab406

[16]Y. Liu et al., “Inkjet-printed unclonable quantum dot fluorescent anti-counterfeiting labels with artificial intelligence authentication,” Nat Commun, vol. 10, no. 1, p. 2409, Jun. 2019, doi: 10.1038/s41467-019-10406-7. Available: http://dx.doi.org/10.1038/s41467-019-10406-7

[17]H. Tian et al., “Piezoelectric actuation for integrated photonics,” Adv. Opt. Photonics, Sep. 2024, doi: 10.1364/aop.529288. Available: http://dx.doi.org/10.1364/aop.529288

[18]J. S. Colton and K. R. Hansen, Eds., Two-dimensional metal Halide perovskites, 2024th ed. Singapore, Singapore: Springer, 2024. doi: 10.1007/978-981-99-7830-4. Available: http://dx.doi.org/10.1007/978-981-99-7830-4

[19]M. P. Cuellar, L. G. B. Ruiz, and M. C. Pegalajar, “Implementation of classical decision trees in a quantum computing paradigm,” in Lecture Notes in Computer Science, in Lecture notes in computer science. Cham: Springer Nature Switzerland, 2024, pp. 226–237. doi: 10.1007/978-3-031-74183-8_19. Available: http://dx.doi.org/10.1007/978-3-031-74183-8_19

[20]D. Solis-Martin, J. Galan-Paez, and J. Borrego-Diaz, “Bayesian model selection pruning in predictive maintenance,” in Lecture Notes in Computer Science, in Lecture notes in computer science. Cham: Springer Nature Switzerland, 2024, pp. 263–274. doi: 10.1007/978-3-031-74183-8_22. Available: http://dx.doi.org/10.1007/978-3-031-74183-8_22

[21]D. E. García and N. DeCastro-García, “Application of transfer learning to online models in malware detection,” in Lecture Notes in Computer Science, in Lecture notes in computer science. Cham: Springer Nature Switzerland, 2024, pp. 177–189. doi: 10.1007/978-3-031-74183-8_15. Available: http://dx.doi.org/10.1007/978-3-031-74183-8_15

[22]H. Goto, “Bifurcation-based adiabatic quantum computation with a nonlinear oscillator network,” Sci Rep, vol. 6, p. 21686, Feb. 2016, doi: 10.1038/srep21686. Available: http://dx.doi.org/10.1038/srep21686

[23]M. R. Habibi, S. Golestan, Y. Wu, J. M. Guerrero, and J. C. Vasquez, “Electrical load forecasting in power systems based on quantum computing using time series-based quantum artificial intelligence,” Sci Rep, vol. 15, no. 1, p. 7429, Mar. 2025, doi: 10.1038/s41598-025-89933-x. Available: http://dx.doi.org/10.1038/s41598-025-89933-x

[24]L. Bischof, S. Teodoropol, R. M. Füchslin, and K. Stockinger, “Hybrid quantum neural networks show strongly reduced need for free parameters in entity matching,” Sci Rep, vol. 15, no. 1, p. 4318, Feb. 2025, doi: 10.1038/s41598-025-88177-z. Available: http://dx.doi.org/10.1038/s41598-025-88177-z

[25]I. D. Lazarev, M. Narozniak, T. Byrnes, and A. N. Pyrkov, “Hybrid quantum-classical unsupervised data clustering based on the self-organizing feature map,” Phys. Rev. A (Coll. Park.), vol. 111, no. 1, Jan. 2025, doi: 10.1103/physreva.111.012416. Available: http://dx.doi.org/10.1103/physreva.111.012416

[26]Y. Wu, J. Yao, P. Zhang, and X. Li, “Ra

ndomness-Enhanced Expressivity of Quantum Neural Networks,” Phys Rev Lett, vol. 132, no. 1, p. 010602, Jan. 2024, doi: 10.1103/PhysRevLett.132.010602. Available: http://dx.doi.org/10.1103/PhysRevLett.132.010602

[27]O. Coskuner-Weber, M. G. Habiboglu, D. Teplow, and V. N. Uversky, “From Quantum Mechanics, Classical Mechanics, and Bioinformatics to Artificial Intelligence Studies in Neurodegenerative Diseases,” Methods Mol Biol, vol. 2340, pp. 139–173, 2022, doi: 10.1007/978-1-0716-1546-1_8. Available: http://dx.doi.org/10.1007/978-1-0716-1546-1_8

[28]B. Dudas and M. A. Miteva, “Computational and artificial intelligence-based approaches for drug metabolism and transport prediction,” Trends Pharmacol Sci, vol. 45, no. 1, pp. 39–55, Jan. 2024, doi: 10.1016/j.tips.2023.11.001. Available: http://dx.doi.org/10.1016/j.tips.2023.11.001

[29]Z. Zhu et al., “Automated Generation and Analysis of Molecular Images Using Generative Artificial Intelligence Models,” J Phys Chem Lett, vol. 15, no. 7, pp. 1985–1992, Feb. 2024, doi: 10.1021/acs.jpclett.3c03504. Available: http://dx.doi.org/10.1021/acs.jpclett.3c03504

[30]A. Fallani, L. Medrano Sandonas, and A. Tkatchenko, “Inverse mapping of quantum properties to structures for chemical space of small organic molecules,” Nat Commun, vol. 15, no. 1, p. 6061, Jul. 2024, doi: 10.1038/s41467-024-50401-1. Available: http://dx.doi.org/10.1038/s41467-024-50401-1

[31]Y. Bian et al., “20 Gbps real-time source-independent quantum random number generator based on a silicon photonic chip,” Opt Lett, vol. 50, no. 4, pp. 1216–1219, Feb. 2025, doi: 10.1364/OL.544982. Available: http://dx.doi.org/10.1364/OL.544982

[32]F. Flamini, M. Krumm, L. J. Fiderer, T. Müller, and H. J. Briegel, “Towards interpretable quantum machine learning via single-photon quantum walks,” Quantum Sci. Technol., vol. 9, no. 4, p. 045011, Oct. 2024, doi: 10.1088/2058-9565/ad5907. Available: http://dx.doi.org/10.1088/2058-9565/ad5907

[33]Y. Li, Y. Zhao, and Y. Zhang, “A spanning tree construction algorithm for industrial wireless sensor networks based on quantum artificial bee colony,” EURASIP J. Wirel. Commun. Netw., vol. 2019, no. 1, Dec. 2019, doi: 10.1186/s13638-019-1496-z. Available: http://dx.doi.org/10.1186/s13638-019-1496-z

[34] L. Ye, Y. Wang, and X. Zheng, “Simulating many-body open quantum systems by harnessing the power of artificial intelligence and quantum computing,” J Chem Phys, vol. 162, no. 12, Mar. 2025, doi: 10.1063/5.0242648. Available: http://dx.doi.org/10.1063/5.0242648

[35]S. Park, H. Baek, and J. Kim, “The matrix: Quantum AI for interacting two worlds in prioritized metaverse spaces,” IEEE Commun. Mag., vol. 62, no. 12, pp. 97–103, Dec. 2024, doi: 10.1109/mcom.001.2300457. Available: http://dx.doi.org/10.1109/mcom.001.2300457

[36]A. Aldossary et al., “In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back,” Adv Mater, vol. 36, no. 30, p. e2402369, Jul. 2024, doi: 10.1002/adma.202402369. Available: http://dx.doi.org/10.1002/adma.202402369

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Publicado

2025-12-27

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Cómo citar

Oviedo Machado, C. A. ., Brito Palmezano, L. D. ., Trillos Sarmiento, W. A. ., & Causil-Contreras, T. I. . (2025). Cuántica e inteligencia artificial: explorando su convergencia desde la cienciometría. Interfaces, 8(1). https://doi.org/10.18041/2619-4465/interfaces.1.13396