Out-of-distribution generalization for learning quantum dynamics

Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are drawn from the same data distribution...

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Detalles Bibliográficos
Autores principales: Caro, Matthias C., Huang, Hsin-Yuan, Ezzell, Nicholas, Gibbs, Joe, Sornborger, Andrew T., Cincio, Lukasz, Coles, Patrick J., Holmes, Zoë
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322910/
https://www.ncbi.nlm.nih.gov/pubmed/37407571
http://dx.doi.org/10.1038/s41467-023-39381-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322910/
https://www.ncbi.nlm.nih.gov/pubmed/37407571
http://dx.doi.org/10.1038/s41467-023-39381-w

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