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Aprender decodificación de errores de alta precisión para procesadores cuánticos
Johannes Bausch1, Andrew W Senior2, Francisco J H Heras3
1Google DeepMind, London, UK. jbausch@google.com.
Nature
|November 20, 2024
Resumen
Un nuevo decodificador de red neuronal mejora significativamente la corrección de errores cuánticos al interpretar con precisión los datos ruidosos de las computadoras cuánticas. Este enfoque de aprendizaje automático mejora la confiabilidad de los cálculos cuánticos y ayuda a construir sistemas cuánticos a gran escala.
Área de la Ciencia:
- La computación cuántica
- Corrección del error cuántico
- Aprendizaje automático
Sus antecedentes:
- La corrección de errores cuánticos es crucial para construir computadoras cuánticas a gran escala.
- Los códigos cuánticos de corrección de errores codifican información redundantemente a través de múltiples qubits.
- La decodificación precisa de la información del síndrome ruidoso es un desafío clave.
Objetivo del estudio:
- Desarrollar un decodificador basado en el aprendizaje automático para el código de superficie, un código líder de corrección de errores cuánticos.
- Para mejorar la precisión de la decodificación de la información del síndrome ruidoso para la computación cuántica.
Principales métodos:
- Desarrolló una red neuronal recurrente basada en transformadores.
- Entrenó la red con datos simulados y del mundo real del procesador cuántico Sycamore de Google.
- Utilizó lecturas suaves e información de fugas para mejorar la decodificación.
Principales resultados:
- El decodificador de la red neuronal superó a los decodificadores de última generación en los datos del mundo real para los códigos de superficie de distancia-3 y distancia-5.
- Se mantiene la ventaja de rendimiento en datos simulados hasta la distancia 11 con ruido realista.
- Adaptación demostrada a distribuciones de error desconocidas utilizando muestras experimentales.
Conclusiones:
- El aprendizaje automático puede superar a los algoritmos diseñados por humanos en la decodificación de errores cuánticos.
- El decodificador desarrollado muestra un gran potencial para su aplicación práctica en ordenadores cuánticos.
- Este trabajo destaca el poder de los enfoques basados en datos en el avance de las tecnologías cuánticas.
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