Zwolak, J.
, Kalantre, S.
, Wu, X.
, Ragole, S.
and Taylor, J.
(2018),
QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments, PLoS One, [online], https://doi.org/10.1371/journal.pone.0205844
(Accessed December 26, 2024)
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QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
Published
Author(s)
, Sandesh Kalantre, , Stephen Ragole,
Citation
PLoS One
Volume
13
Issue
10
Pub Type
Journals
Download Paper
Keywords
machine learning, dataset, convolutional neural networks, software, quantum dots
Citation
Issues
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Created October 17, 2018, Updated November 10, 2018