Publications

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D. M. Zajac, Sigillito, A. J., Russ, M., Borjans, F., Taylor, J. M., Burkard, G., and Petta, J. R., Resonantly driven CNOT gate for electron spins, Science, vol. 359, no. 6374, pp. 439-442, 2018.
E. Zeuthen, Schliesser, A., Sørensen, A. S., and Taylor, J. M., Figures of merit for quantum transducers, 2016.
E. Zeuthen, Schliesser, A., Taylor, J. M., and Sørensen, A. S., Electro-optomechanical equivalent circuits for quantum transduction, 2018.
E. Zeuthen, Gullans, M., Maghrebi, M. F., and Gorshkov, A. V., Correlated Photon Dynamics in Dissipative Rydberg Media, Physical Review Letters, vol. 119, no. 4, p. 043602, 2017.
B. Zhan, Kimmel, S., and Hassidim, A., Super-Polynomial Quantum Speed-ups for Boolean Evaluation Trees with Hidden Structure, ITCS '12 Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 249-265, 2012.
Y. Zhang, Shalm, L. K., Bienfang, J. C., Stevens, M. J., Mazurek, M. D., Nam, S. Woo, Abellán, C., Amaya, W., Mitchell, M. W., Fu, H., Miller, C. A., Mink, A., and Knill, E., Experimental Low-Latency Device-Independent Quantum Randomness, 2018.
J. Zhang, Pagano, G., Hess, P. W., Kyprianidis, A., Becker, P., Kaplan, H., Gorshkov, A. V., Gong, Z. - X., and Monroe, C., Observation of a Many-Body Dynamical Phase Transition with a 53-Qubit Quantum Simulator, Nature, vol. 551, pp. 601-604, 2017.
E. Zhao, Bray-Ali, N., Williams, C. J., Spielman, I. B., and Satija, I. I., Chern numbers hiding in time-of-flight images, Physical Review A, vol. 84, no. 6, 2011.
T. Zhou, Xu, S., Chen, X., Guo, A., and Swingle, B., The operator Lévy flight: light cones in chaotic long-range interacting systems, 2019.
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.
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, 2019.
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.