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D. Zhu, Johri, S., Nguyen, N. H., C. Alderete, H., Landsman, K. A., Linke, N. M., Monroe, C., and Matsuura, A. Y., “Probing many-body localization on a noisy quantum computer”, 2020.
S. Zhu, Hung, S. - H., Chakrabarti, S., and Wu, X., “On the Principles of Differentiable Quantum Programming Languages”, 2020.
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.
L. A. Zhukas, Wang, Q., Katz, O., Monroe, C., and Marvian, I., “Observation of the Symmetry-Protected Signature of 3-body Interactions”, 2024.
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.
T. Zolkin, Kharkov, Y., and Nagaitsev, S., “Machine-assisted discovery of integrable symplectic mappings”, 2022.
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.
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.
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.
J. P. Zwolak and Taylor, J. M., “Colloquium: Advances in automation of quantum dot devices control”, Reviews of Modern Physics, vol. 95, 2023.
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.
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.