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E. Zeuthen, Schliesser, A., Taylor, J. M., and Sørensen, A. S., “Electro-optomechanical equivalent circuits for quantum transduction”, 2018.
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.
C. Zhang, Leng, J., and Li, T., “Quantum Algorithms for Escaping from Saddle Points”, Quantum, vol. 5, no. 529, 2021.
Y. Zhang, Fu, H., and Knill, E., “Efficient randomness certification by quantum probability estimation”, Phys. Rev. Research , vol. 2, no. 013016, 2020.
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., Mink, A., and Knill, E., “Experimental Low-Latency Device-Independent Quantum Randomness”, Phys. Rev. Lett. , vol. 124, no. 010505, 2020.
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.
Q. Zhao, Zhou, Y., Shaw, A. F., Li, T., and Childs, A. M., “Hamiltonian simulation with random inputs”, Phys. Rev. Lett. 129, 270502, vol. 129, no. 270502, 2022.
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.
Q. Zhao and Zhou, Y., “Constructing Multipartite Bell inequalities from stabilizers”, 2020.
Q. Zhao and Yuan, X., “Exploiting anticommutation in Hamiltonian simulation”, 2021.
Q. Zhao, Zhou, Y., and Childs, A. M., “Entanglement accelerates quantum simulation”, 2024.
W. Zhong, Gold, J. M., Marzen, S., England, J. L., and Halpern, N. Yunger, “Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive”, Scientific Reports, vol. 11, 2021.
T. Zhou, Xu, S., Chen, X., Guo, A., and Swingle, B., “The operator Lévy flight: light cones in chaotic long-range interacting systems”, Phys. Rev. Lett. , vol. 124, no. 180601, 2020.
Y. Zhou, Xiao, B., Da Li, M. -, Zhao, Q., Yuan, Z. - S., Ma, X., and Pan, J. - W., “A scheme to create and verify scalable entanglement in optical lattice”, npj Quantum Information, vol. 8, 2022.
J. Zhou, Criswell, J., and Hicks, M., “Fat Pointers for Temporal Memory Safety of C”, Proceedings of the ACM on Programming Languages, vol. 7, no. 1, pp. 316-347, 2023.
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.
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.
D. Zhu, Kahanamoku-Meyer, G. D., Lewis, L., Noel, C., Katz, O., Harraz, B., Wang, Q., Risinger, A., Feng, L., Biswas, D., Egan, L., Gheorghiu, A., Nam, Y., Vidick, T., Vazirani, U., Yao, N. Y., Cetina, M., and Monroe, C., “Interactive Protocols for Classically-Verifiable Quantum Advantage”, 2021.
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.
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, 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, 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.