Publications

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Journal Article
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.
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, PRX Quantum, vol. 2, no. 020335, 2021.
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, 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.
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, 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.