Prediction and real-time compensation of qubit decoherence via machine learning
Control engineering techniques are promising for realizing stable quantum systems to counter their extreme fragility. Here the authors use techniques from machine learning to enable real-time feedback suppression of decoherence in a trapped ion qubit by predicting its future stochastic evolution.
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2017
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/54fb8f67b9664aab884a9c4affc37390 |
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| Sumario: | Control engineering techniques are promising for realizing stable quantum systems to counter their extreme fragility. Here the authors use techniques from machine learning to enable real-time feedback suppression of decoherence in a trapped ion qubit by predicting its future stochastic evolution. |
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