Inferencia bayesiana, inteligencia artificial y computación cuántica: una revisión sistemática de la literatura
DOI:
https://doi.org/10.63688/qnj40m25Palabras clave:
inferencia bayesiana, optimización bayesiana, computación cuántica, aprendizaje automático cuántico, revisión sistemáticaResumen
Esta revisión sistemática examina la integración sustantiva entre inferencia bayesiana, inteligencia artificial/aprendizaje automático y computación cuántica, con énfasis en los avances metodológicos, las estructuras matemáticas, las aplicaciones y la solidez de la evidencia. Se analizaron publicaciones de 2010 a 2025 recuperadas de Scopus y Web of Science. El protocolo siguió PSALSAR; la selección se reportó mediante PRISMA 2020 y la búsqueda se documentó con PRISMA-S. De 390 registros iniciales, 261 permanecieron después de normalización, deduplicación y control temporal; se evaluaron 119 textos completos y 66 artículos integraron la síntesis principal. RQ1-RQ4 identificaron ocho familias de avance y una taxonomía funcional que separa técnica bayesiana, tarea de IA, paradigma cuántico, arquitectura y objeto matemático. Predominaron la optimización bayesiana de modelos, circuitos o controles cuánticos (27 artículos; 40,9 %), las arquitecturas híbridas clásico-cuánticas (49; 74,2 %) y las tareas de optimización (25; 37,9 %). RQ5 encontró solo dos aplicaciones financieras o económicas directas (3,0 %) y ninguna aplicación macroeconómica directa. RQ6 mostró una evidencia dominada por simulaciones (33; 50,0 %); 19 estudios incluyeron hardware, pero únicamente 11 lo utilizaron como validación principal. La madurez fue principalmente intermedia (34; 51,5 %) y la reproducibilidad permaneció limitada: 25 artículos reportaron código y 14 alcanzaron la puntuación superior de reproducibilidad. Los resultados más sólidos corresponden a mejoras locales en fidelidad, presupuesto de mediciones, mezcla de cadenas, representación probabilística o compactación de circuitos; no sustentan una ventaja cuántica general. Como contribución de síntesis, se formaliza un ciclo híbrido común y se proponen criterios de comparación que preservan la naturaleza ordinal de la evidencia y distinguen costo, error, escala y validación de dominio.
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Derechos de autor 2026 Carla Pazmiño Naranjo, Andrés Galvis Correa (Autor/a)
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