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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.
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, 2021.
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.
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.
T. Zolkin, Kharkov, Y., and Nagaitsev, S., Machine-assisted discovery of integrable symplectic mappings, 2022.
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.
S. Zhu, Hung, S. - H., Chakrabarti, S., and Wu, X., On the Principles of Differentiable Quantum Programming Languages, 2020.
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.
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, 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.
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.
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.
J. Zhou, Criswell, J., and Hicks, M., Fat Pointers for Temporal Memory Safety of C, 2022.
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.
Q. Zhao and Zhou, Y., Constructing Multipartite Bell inequalities from stabilizers, 2020.
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 Yuan, X., Exploiting anticommutation in Hamiltonian simulation, 2021.
Q. Zhao, Zhou, Y., Shaw, A. F., Li, T., and Childs, A. M., Hamiltonian simulation with random inputs, 2021.
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.
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.
Y. Zhang, Fu, H., and Knill, E., Efficient randomness certification by quantum probability estimation, Phys. Rev. Research , vol. 2, no. 013016, 2020.
C. Zhang, Leng, J., and Li, T., Quantum Algorithms for Escaping from Saddle Points, Quantum, vol. 5, no. 529, 2021.
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.
E. Zeuthen, Schliesser, A., Sørensen, A. S., and Taylor, J. M., Figures of merit for quantum transducers, 2016.