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Justyna Zwolak


Justyna Zwolak is a Scientist in the Applied and Computational Mathematics Division at National Institute of Standards and Technology in Gaithersburg, MD. She 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. She subsequently was a research associate in the Department of Physics at Oregon State University, at the STEM Transformation Institute at Florida International University, and an assistant research scholar in the Joint Center for Quantum Information and Computer Science at University of Maryland on College Park, MD. 

Her past research pursuits range from quantum information theory and machine learning to complex network analysis to mathematics and physics education. In particular, she developed novel approaches to characterizing entanglement in quantum systems and delineating the space of quantum states. Using recursive techniques and linear algebra, she proved that classes of linear maps hold certain mathematical properties (positive, but not completed positive, optimal, etc.), which enabled extending so-called entanglement witnesses into high-dimensional composite systems. She also led efforts to employ and develop network and statistical analyses to identifying factors that affect student persistence in introductory physics courses. This work resulted in a number of surprising findings (for instance, that social integration is more important than grades in predicting persistence for certain cohorts of students). Her work has been highlighted in Science, Nature Physics, and as the "Editor's Choice" in Physical Review.  

Justyna's current research uses 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. Success in this endeavor will eliminate the need for heuristic calibration and help scale up quantum computing into larger quantum dot arrays.


Auto-tuning of double dot devices it in situ with machine learning

Justyna P. Zwolak, Thomas McJunkin, Sandesh Kalantre, J. P. Dodson, E. R. MacQuarrie, D. E. Savage, M. G. Lagally, S N. Coppersmith, Mark A. Eriksson, Jacob M. Taylor
The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time- consuming procedure that is inherently impractical for
Created April 23, 2019, Updated March 31, 2020