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Justyna P. Zwolak (Fed)
Mathematician
Justyna Zwolak is a Scientist in the Technology Test and Evaluation Division at the 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 served as a research associate in the Department of Physics at Oregon State University, a research associate 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 the University of Maryland in College Park, MD.
Her current research uses machine learning algorithms and super 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.
Justyna's 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 completely 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 identify factors that affect student persistence in introductory physics courses. This work resulted in several surprising findings (for instance, that social integration is more important than grades in predicting persistence among certain student cohorts). Her work has been highlighted in Science, Nature Physics, and as the "Editor's Choice" in Physical Review.
Awards
Presidential Early Career Award for Scientists and Engineers (January 2025)
2024 Department of Commerce Bronze Medal Award (January 2025, Team Award)
"For pioneering the field of machine learning for quantum control."
ITL Best Journal Paper of the Year Award (July 2024)
"For your jointly authored paper “Colloquium: Advances in automation of quantum dot devices control”
published in Reviews of Modern Physics in February 2023."
2024 Award for Excellence in Research in Applied Mathematics (May 2024)
"For outstanding contributions in the application of machine learning
to the control of systems at the frontiers of quantum science and technology."
