The revolution in artificial intelligence. From classical to quantum

Authors

Keywords:

Artificial intelligence, Classical, Quantu, Algorithms, technology

Abstract

This article focuses on the transformation of artificial intelligence (AI) from its beginnings in classical paradigms to its contemporary development in the context of quantum computing. As artificial intelligence has evolved, it has proven to be an essential tool in various sectors, from health going through education to finance. The main purpose of this article is to explore the evolution of artificial intelligence from its classical foundations to emerging innovations in quantum computing. This is a qualitative research, framed in a methodology of a type of documentary study, using the technique of documentary review, which consisted of a critical and systematic analysis of previously prepared documents, with the purpose of extracting pertinent and relevant information, making it possible to detail the analyzed phenomenon by characterizing this technology. One of the main findings emanating from this research reveals that quantum algorithms can perform computational tasks that, in their classical version, would consume an exorbitant amount of time. This way, it is concluded that the integration of quantum computing into artificial intelligence is not only viable, but could also mark a before and after in the field. However, the need to continue researching the practical implementation and scalability of these technologies is emphasized so that their potential is fully realized, opening a new horizon for the development of innovative solutions in the different disciplines of science, technology and scientific research.

Author Biography

  • Maritza Núñez, Francisco de Miranda National Experimental University

    maritza.nunez.696@gmail.com

References

Arias, F. (2012). Proyecto de investigación. Introducción a la metodología científica. Editorial Pontificia Episteme. 6ta edición. Caracas, Venezuela.

Arute, F.; Arya, K.; Babbush, R.; Bacon, J.; Bardin, J.; Bar ends, R. y Martinis, J. (2019). Quantum supremacy using a programmable superconducting processor. Nature. Disponi ble en: https://pubmed.ncbi.nlm.nih.gov/31645734/.

Babbush, R., et al. (2018). Encoding quantum operators in quantcircuits. Physical Review.https://www.researchgate. net/publication/328469422_Encoding_Electroni pectra_in_ Quantum_Circuits.

Benedetti, M., Lloyd, S., & Florentine, M. (2021). Parame terized quantum circuits as machine learning models. Quan tum Science and Technology, 6(1), 1-15. Chuang, I. y Nielsen, M. (2010). Quantum Computation and Quantum Information. Disponible en: https://profm cruz.wordpress.com/wp-content/uploads/2017/08/quan tum computation and-quantum-information-nielsen-ch uang.pdf.

CIFAR. (2018). Program on Quantum Information Sci ence. Disponible en: https://cifar.ca/researchprograms/ quantum-information-science/.

Denzin, N. y Lincoln, Y. (2021). The SAGE Handbook of Qualitative Research (4th Ed.). Sage Publications.

Eket, A.; Ignacio, C.; Lamata, L.; Minguez, J.; Lenaham, B. y Barg, S. (2023). Quantum Computing e Inteligencia Arti f icial: la revolución silenciosa. Disponible en: https://www. fundacionbankinter.org/wp-content/uploads/2023/03/ Informe-FTF-The-Silent-Revolution-of-Quantum-Compu ting-AI.pdf.

Farhi, E. y Harrow, A. (2016). Quantum Algorithms for Fixed Qubit Architectures. arXiv preprint arXiv:1602.07674.

Grover, L. (1996). A fast quantum mechanical algorithm for database search. Proceedings of the 28th Annual ACM Symposium on Theory of Computing, 212-219.

Hangleiter, D.; Kottmann, J. y Miller, D. (2020). Quantum models of neural networks. Machine Learning: Science and Technology, 1(1), 25. doi:10.1088/2632-2153/ab6e8b.

Harrow, A. (2009). Quantum algorithms for fixed Qubit architectures. Nature, (7252), 464-468.

Hernández, R., Fernández, C. y Baptista, P. (2014). Me todología de la investigación (6th ed.). McGraw-Hill. ISBN: 978-1-4562-6096-5.

Job, J. (2020). Ethical implications of quantum comput ing: Risks and challenges. Frontiers in Quantum Computing.

Jordan, S. P., et al. (2019). Quantum algorithms for fixed Qubit Architectures. Nature Reviews Physics, 1(9), 514-527. Disponible en: https://ar5iv.labs.arxiv.org/html/1703.06199. Krizhevsky, A., Sutskever, I. y Hinton, G. (2012). Ima geNet classification with deep convolutional neural net works. Advances in Neural Information Processing Sys tems.Disponible en: https://www.researchgate.net/publication/267960550_ImageNet_Classification_with Deep_Convolutional_Neural_Networks.

Kumar, A., Gupta, R. y Ghosh, P. (2020). Natural Lan guage Processing: A Comprehensive Review. Journal of Com puter Science and Technology, 35(3), 357-388.

Lloyd, S. (2007). Quantum Information and the Future of Quantum Computing. Nature Physics, 3(12), 752-754.

Macías, Y. (2021). La tecnología y la Inteligencia Arti f icial en el sistema educativo. Disponible en: https://core. ac.uk/download/481436033.pdf. McCarthy, J., Minsky, M., Rochester, N. y Shannon, C. (1955). A Proposal for the Dartmouth Summer Research Proj ect on Artificial Intelligence. Disponible en: http://jmc.stan ford.edu/articles/dartmouth/dartmouth.pdf. EE.UU.

Moreno, F. (2023). Inteligencia artificial cuántica. Blog. Preskill, J. (2018). Quantum Computing in the NISQ era and beyond. Disponible en: https://quantum-journal.org/ papers/q-2018-08-06-79/.

Rebentrost, P., et al. (2014). Quantum machine learn ing with quantum neural networks. Physical Review Let ters, 113(14). Disponible en: https://www.google.com/ search?q=Rebentrost%2C+P.%2C+et+al.+(2014).+Quantm+machine+learning+with+quantum+neural+net works.+Physical.

Sharma, G., Yadav, A. y Chopra, R. (2020). Artificial intelligence and effective governance: A review, critique and research agenda. Sustainable Futures. Disponible en: https://www.sciencedirect.com/science/article/pii/ S2666188819300048sec0001. Shor, P. (1997). Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum comput er. SIAM Journal on Computing, 26(5), 1484-1509.

Shulman, J. (2023). Artificial Intelligence in Education: Supporting the Learning Experience. Educational Technology Research and Development. Disponible en: https://www. researchgate.net/publication/369288544_Artificial_intel ligence_in_edution.

Silver, D., Huang, A., Maddison, J., Guez, A. (2016). Mas tering the Game of Go with Deep Neural Networks and Tree Search. Nature. Disponible en: https://doi.org/10.1038/na ture16961.

Turing, A. (1950). Computing Machinery and In telligence. Mind, 59(236). Disponible en: https://doi. org/10.1093/mind/LIX.236.433.

Inteligencia artificial

Downloads

Published

2026-04-07

Issue

Section

Artículos científicos

How to Cite

The revolution in artificial intelligence. From classical to quantum. (2026). Observador Del Conocimiento, 10(3), 106-117. https://revistaoac-cal.oncti.gob.ve/index.php/ODC/article/view/33

Similar Articles

1-10 of 160

You may also start an advanced similarity search for this article.