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Search Publications by: Justyna P. Zwolak (Fed)

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Displaying 26 - 39 of 39

Machine-learning enhanced dark soliton detection in Bose-Einstein condensates

June 15, 2021
Author(s)
Shangjie Guo, Amilson R. Fritsch, Ian Spielman, Justyna Zwolak
Most data in cold-atom experiments comes from images, the analysis of which is limited by our preconceptions of the patterns that could be present in the data. We focus on the well-defined case of detecting dark solitons—appearing as local density

Ray-based framework for state identification in quantum dot devices

June 7, 2021
Author(s)
Justyna Zwolak, Thomas McJunkin, Sandesh Kalantre, Samuel Neyens, Evan MacQuarrie, Mark A. Eriksson, Jacob Taylor
Quantum dots (QDs) defined with electrostatic gates are a leading platform for a scalable quantum computing implementation. However, with increasing numbers of qubits, the complexity of the control parameter space also grows. Traditional measurement

Survey on Gender, Equity and Inclusion

March 29, 2021
Author(s)
Mary Frances Theofanos, Jasmine Evans, Justyna Zwolak, Sandra Spickard Prettyman
In the fall of 2019, the National Institute of Standards and Technology (NIST) funded threes studies to better understand equity and inclusivity. The present study represents phase three of a sequential, exploratory mixed methods study designed to provide

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

March 31, 2020
Author(s)
Justyna Zwolak, Thomas McJunkin, Sandesh Kalantre, J. P. Dodson, Evan MacQuarrie, D. E. Savage, M. G. Lagally, S N. Coppersmith, Mark A. Eriksson, Jacob Taylor
The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time- consuming procedure that is inherently impractical for scaling up and applications. In this work, we report on the \it in situ} implementation of a

Ray-based classification framework for high-dimensional data

February 3, 2020
Author(s)
Justyna Zwolak, Jacob Taylor, Sandesh Kalantre, Thomas McJunkin, Brian Weber
While classification of arbitrary structures in high dimensions may require complete quantitative information, for simple geometrical structures, low-dimensional qualitative information about the boundaries defining the structures can suffice. Rather than

Machine Learning techniques for state recognition and auto-tuning in quantum dots

January 20, 2019
Author(s)
Sandesh Kalantre, Justyna Zwolak, Stephen Ragole, Xingyao Wu, Neil M. Zimmerman, Michael Stewart, Jacob Taylor
Recent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good experimental parameters, i.e. tuning up devices. In many cases, including in