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Aprendizaje automático para la estimación y el control de sistemas cuánticos

Hailan Ma1,2, Bo Qi3,4, Ian R Petersen1

  • 1School of Engineering, Australian National University, Canberra, ACT 2601, Australia.

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Resumen
Este resumen es generado por máquina.

El aprendizaje automático mejora las tecnologías cuánticas al mejorar el control y la estimación de sistemas cuánticos complejos. Esta revisión cubre las redes neuronales, los métodos de gradiente, el cálculo evolutivo y el aprendizaje por refuerzo para tareas cuánticas.

Palabras clave:
Aprendizaje automáticored neuronalcontrol cuánticoestimación cuánticamedición cuánticaAprendizaje por refuerzo

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Área de la Ciencia:

  • Ciencia de la información cuántica
  • Inteligencia artificial
  • Teoría de control

Sus antecedentes:

  • El avance de las tecnologías cuánticas requiere un sofisticado control y calibración de sistemas cuánticos complejos.
  • El aprendizaje automático (ML) ofrece poderosos enfoques basados en datos para abordar estos desafíos.
  • La estimación y el control cuánticos son críticos para realizar la computación cuántica, la simulación y la detección.

Objetivo del estudio:

  • Revisar las aplicaciones significativas del aprendizaje automático en la estimación y el control cuánticos.
  • Resaltar las técnicas de ML para mejorar la eficiencia y la robustez de los sistemas cuánticos.
  • Proporcionar una visión general de la investigación actual en la intersección de ML y el control cuántico.

Principales métodos:

  • Estimación del estado cuántico basada en la red neuronal.
  • El control óptimo basado en gradientes cuánticos.
  • Cálculo evolutivo para el aprendizaje de control de sistemas cuánticos.
  • Aprendizaje automático para el control cuántico robusto.
  • Aprendizaje de refuerzo para el control cuántico adaptativo.

Principales resultados:

  • Los métodos ML demuestran capacidades significativas en el aprendizaje de dinámicas cuánticas complejas.
  • Las redes neuronales son prometedoras para una estimación precisa del estado cuántico.
  • Los métodos gradientes y evolutivos ofrecen vías eficientes para el control óptimo cuántico.
  • El aprendizaje por refuerzo permite estrategias de control adaptativo para sistemas cuánticos.

Conclusiones:

  • El aprendizaje automático es una herramienta transformadora para el avance de las tecnologías cuánticas.
  • La integración de ML con el control y la estimación cuánticos es crucial para los futuros sistemas cuánticos.
  • La investigación adicional en el control cuántico impulsado por ML acelerará el progreso en la computación cuántica, la simulación y la detección.