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Justyna P. Zwolak
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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.
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Journal Article
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.
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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.
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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.
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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.
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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.
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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.
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R. Dou
and
Zwolak, J. P.
,
“
Practitioner's guide to social network analysis: Examining physics anxiety in an active-learning setting
”
, 2018.
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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.
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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.
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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.
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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.
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J. P. Zwolak
and
Taylor, J. M.
,
“
Colloquium: Advances in automation of quantum dot devices control
”
,
Reviews of Modern Physics
, vol. 95, 2023.
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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.
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