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QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments

Published

Author(s)

Justyna P. Zwolak, Sandesh Kalantre, Xingyao Wu, Stephen Ragole, Jacob M. Taylor
Citation
PLoS One
Volume
13
Issue
10

Keywords

machine learning, dataset, convolutional neural networks, software, quantum dots

Citation

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 November 30, 2021)
Created October 17, 2018, Updated November 10, 2018