Publications

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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.
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., 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 and Taylor, J. M., Colloquium: Advances in automation of quantum dot devices control, Reviews of Modern Physics, vol. 95, 2023.
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.
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.
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.
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. 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.
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.
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.
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., Shaw, A. F., Li, T., and Childs, A. M., Hamiltonian simulation with random inputs, Phys. Rev. Lett. 129, 270502, vol. 129, no. 270502, 2022.
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.
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.