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Publications

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Filters: Author is Justyna P. Zwolak  [Clear All Filters]
2023
J. P. Zwolak and Taylor, J. M., “Colloquium: Advances in automation of quantum dot devices control”, Reviews of Modern Physics, vol. 95, 2023.
J. P. Zwolak, Taylor, J. M., Andrews, R., Benson, J., Bryant, G., Buterakos, D., Chatterjee, A., Sarma, S. Das, Eriksson, M. A., Greplová, E., Gullans, M., Hader, F., Kovach, T. J., Mundada, P. S., Ramsey, M., Rasmussen, T., Severin, B., Sigillito, A., Undseth, B., and Weber, B., “Data Needs and Challenges of Quantum Dot Devices Automation: Workshop Report”, 2023.
2022
S. Guo, Koh, S. M., Fritsch, A. R., Spielman, I. B., and Zwolak, J. P., “Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons”, Phys. Rev. Research, vol. 4, p. 023163 , 2022.
A. R. Fritsch, Guo, S., Koh, S. M., Spielman, I. B., and Zwolak, J. P., “Dark Solitons in Bose-Einstein Condensates: A Dataset for Many-body Physics Research”, 2022.
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”, SN Comput. Sci., vol. 3, no. 57, 2022.
J. Ziegler, McJunkin, T., Joseph, E. S., Kalantre, S. S., Harpt, B., Savage, D. E., Lagally, M. G., Eriksson, M. A., Taylor, J. M., and Zwolak, J. P., “Toward Robust Autotuning of Noisy Quantum dot Devices”, Physical Review Applied, vol. 17, 2022.
2021
S. Guo, Fritsch, A. R., Greenberg, C., Spielman, I. B., and Zwolak, J. P., “Machine-learning enhanced dark soliton detection in Bose-Einstein condensates”, Mach. Learn.: Sci. Technol. , vol. 2, p. 035020, 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.
2020
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.
J. P. Zwolak, Kalantre, S. S., McJunkin, T., Weber, B. J., and Taylor, J. M., “Ray-based classification framework for high-dimensional data”, Proceedings of the Machine Learning and the Physical Sciences Workshop at NeurIPS 2020, Vancouver, Canada, 2020.
2018
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.
R. Dou and Zwolak, J. P., “Practitioner's guide to social network analysis: Examining physics anxiety in an active-learning setting”, 2018.
C. A. Hass, Genz, F., Kustusch, M. Bridget, Ouime, P. - P. A., Pomian, K., Sayre, E. C., and Zwolak, J. P., “Studying community development: a network analytical approach”, 2018.
2017
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.