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Aprendizaje automático cuántico

Jacob Biamonte1,2, Peter Wittek3, Nicola Pancotti4

  • 1Quantum Complexity Science Initiative, Skolkovo Institute of Science and Technology, Skoltech Building 3, Moscow 143026, Russia.

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|September 15, 2017
PubMed
Resumen
Este resumen es generado por máquina.

La computación cuántica puede acelerar el aprendizaje automático aprovechando patrones cuánticos únicos. Los investigadores están desarrollando algoritmos cuánticos de aprendizaje automático, pero siguen existiendo obstáculos significativos de hardware y software para las aplicaciones prácticas.

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Área de la Ciencia:

  • Ciencias de la computación
  • Física Cuántica
  • Inteligencia artificial

Sus antecedentes:

  • El aprendizaje automático (ML) utiliza el poder computacional y los algoritmos para identificar patrones de datos.
  • Los sistemas cuánticos exhiben patrones únicos que potencialmente ofrecen ventajas sobre los sistemas clásicos para las tareas de ML.

Objetivo del estudio:

  • Explorar el potencial de la computación cuántica para mejorar el aprendizaje automático.
  • Investigar el desarrollo de algoritmos y software de aprendizaje cuántico de máquinas (QML).

Principales métodos:

  • Revisión de los avances actuales en los algoritmos cuánticos relevantes para el ML.
  • Análisis de los fundamentos teóricos de la ventaja cuántica en el reconocimiento de patrones.

Principales resultados:

  • Los algoritmos cuánticos son prometedores como componentes fundamentales para los programas de aprendizaje automático.
  • Se supone que los sistemas cuánticos procesan de manera eficiente patrones intratables para las computadoras clásicas.

Conclusiones:

  • El aprendizaje automático cuántico es un campo prometedor con el potencial de acelerar significativamente el aprendizaje automático clásico.
  • Se deben superar desafíos sustanciales de hardware y software para realizar aplicaciones prácticas de aprendizaje automático cuántico.