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Justyna P. Zwolak
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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
,
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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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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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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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.
,
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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