Publications

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Author Title [ Type(Desc)] Year
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Journal Article
J. P. Zwolak, McJunkin, T., Kalantre, S. S., Dodson, J. P., MacQuarrie, E. R., Savage, D. E., Lagally, M. G., Coppersmith, S. N., Eriksson, M. A., and Taylor, J. M., Auto-tuning of double dot devices in situ with machine learning, Phys. Rev. Applied , vol. 13, no. 034075 , 2020.
S. S. Kalantre, Zwolak, J. P., Ragole, S., Wu, X., Zimmerman, N. M., Stewart, M. D., and Taylor, J. M., Machine Learning techniques for state recognition and auto-tuning in quantum dots, 2017.
J. P. Zwolak, Kalantre, S. S., Wu, X., Ragole, S., and Taylor, J. M., QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments, PLOS ONE, vol. 13, no. 10, p. e0205844, 2018.
J. P. Zwolak, Kalantre, S. S., McJunkin, T., Weber, B. J., and Taylor, J. M., Ray-based classification framework for high-dimensional data, 2020.
J. P. Zwolak, McJunkin, T., Kalantre, S. S., Neyens, S. F., MacQuarrie, E. R., Eriksson, M. A., and Taylor, J. M., Ray-based framework for state identification in quantum dot devices, 2021.
B. J. Weber, Kalantre, S. S., McJunkin, T., Taylor, J. M., and Zwolak, J. P., Theoretical bounds on data requirements for the ray-based classification, 2021.