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The fundamentals of quantum machine learning

Huang, Bing and Symonds, Nadine O. and von Lilienfeld, Anatole. (2018) The fundamentals of quantum machine learning. Handbook of Materials Modeling. pp. 1-27.

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Official URL: https://edoc.unibas.ch/68678/

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Abstract

Within the past few years, we have witnessed the rising of quantum machine learning (QML) models which infer electronic properties of molecules and materials, rather than solving approximations to the electronic Schrödinger equation. The increasing availability of large quantum mechanics reference datasets has enabled these developments. We review the basic theories and key ingredients of popular QML models such as choice of regressor, data of varying trustworthiness, the role of the representation, and the effect of training set selection. Throughout we emphasize the indispensable role of learning curves when it comes to the comparative assessment of different QML models.
Faculties and Departments:05 Faculty of Science > Departement Chemie
05 Faculty of Science > Departement Chemie > Chemie > Physikalische Chemie (Lilienfeld)
UniBasel Contributors:Huang, Bing and von Lilienfeld, Anatole and Symonds, Nadine Olivia
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Springer
Note:Publication type according to Uni Basel Research Database: Journal article
Identification Number:
Last Modified:09 Jul 2019 13:47
Deposited On:09 Jul 2019 13:47

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