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D. Zhu
,
Cian, Z. - P.
,
Noel, C.
,
Risinger, A.
,
Biswas, D.
,
Egan, L.
,
Zhu, Y.
,
Green, A. M.
,
Alderete, C. Huerta
,
Nguyen, N. H.
,
Wang, Q.
,
Maksymov, A.
,
Nam, Y.
,
Cetina, M.
,
Linke, N. M.
,
Hafezi, M.
, and
Monroe, C.
,
“
Cross-Platform Comparison of Arbitrary Quantum Computations
”
, 2021.
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B. Zhu
,
Gadway, B.
,
Foss-Feig, M.
,
Schachenmayer, J.
,
Wall, M.
,
Hazzard, K. R. A.
,
Yan, B.
,
Moses, S. A.
,
Covey, J. P.
,
Jin, D. S.
,
Ye, J.
,
Holland, M.
, and
Rey, A. Maria
,
“
Suppressing the loss of ultracold molecules via the continuous quantum Zeno effect
”
,
Physical Review Letters
, vol. 112, no. 7, 2014.
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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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T. Zolkin
,
Kharkov, Y.
, and
Nagaitsev, S.
,
“
Machine-assisted discovery of integrable symplectic mappings
”
, 2022.
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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
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
,
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.
,
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.
,
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
,
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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