Justyna Zwolak

justyna-zwolak's picture
Adjunct Assistant Professor
jpzwolak@umd.edu

Justyna Zwolak is an adjunct assistant professor in the Institute for Advanced Computer Studies (UMIACS). She is also a Mathematician at NIST and a QuICS Affiliate Fellow. Her research focuses on using machine learning algorithms and artificial intelligence, especially deep convolutional neural networks, in quantum computing platforms. In particular, she is investigating methods to automatically identify stable configurations of electron spins in semiconductor-based quantum computing. She is also developing a complete software suite that enables modeling of quantum dot devices, train recognition networks, and -- through mathematical optimization -- auto-tune experimental setups.

Justyna received an M.Sc. in Mathematics from The Faculty of Mathematics and Informatics, Nicolaus Copernicus University, and a Ph.D. in Physics from the Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, in Toruń, Poland.

Publications

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.
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.
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.

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.
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.
R. Dou and Zwolak, J. P., “Practitioner's guide to social network analysis: Examining physics anxiety in an active-learning setting”, 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.