Generalization in quantum machine learning from few training data

Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generalizing). In this work, we provide a comprehensive study of generalization performance in QML a...

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Detalles Bibliográficos
Autores principales: Caro, Matthias C., Huang, Hsin-Yuan, Cerezo, M., Sharma, Kunal, Sornborger, Andrew, Cincio, Lukasz, Coles, Patrick J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9395350/
https://www.ncbi.nlm.nih.gov/pubmed/35995777
http://dx.doi.org/10.1038/s41467-022-32550-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9395350/
https://www.ncbi.nlm.nih.gov/pubmed/35995777
http://dx.doi.org/10.1038/s41467-022-32550-3

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