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Sandesh S. Kalantre
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2022
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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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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2021
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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2020
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
,
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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2018
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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2017
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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