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L. Li, Zhu, M., Cleaveland, R., Nicolellis, A., Lee, Y., Chang, L., and Wu, X., Qafny: A Quantum-Program Verifier, 2024.
L. Li, Zhu, M., Cleaveland, R., Lee, Y., Chang, L., and Wu, X., Qafny: Quantum Program Verification Through Type-guided Classical Separation Logic, 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.
X. Wang, Wilde, M. M., and Su, Y., Quantifying the magic of quantum channels, New Journal of Physics, vol. 21, no. 103002, 2019.
X. Wang, Wilde, M. M., and Su, Y., Quantifying the magic of quantum channels, New Journal of Physics, vol. 21, no. 103002, 2019.
S. - H. Hung, Hietala, K., Zhu, S., Ying, M., Hicks, M., and Wu, X., Quantitative Robustness Analysis of Quantum Programs (Extended Version), Proc. ACM Program. Lang., vol. 3, no. POPL, p. Article 31, 2018.
L. Li, Chang, L., Cleaveland, R., Zhu, M., and Wu, X., The Quantum Abstract Machine, 2024.
M. Jarret, Lackey, B., Liu, A., and Wan, K., Quantum adiabatic optimization without heuristics, 2018.
S. Chakrabarti, Childs, A. M., Hung, S. - H., Li, T., Wang, C., and Wu, X., Quantum algorithm for estimating volumes of convex bodies, ACM Transactions on Quantum Computing, vol. 4, 2023.
S. Chakrabarti, Childs, A. M., Hung, S. - H., Li, T., Wang, C., and Wu, X., Quantum algorithm for estimating volumes of convex bodies, ACM Transactions on Quantum Computing, vol. 4, 2023.
D. W. Berry, Childs, A. M., Ostrander, A., and Wang, G., Quantum algorithm for linear differential equations with exponentially improved dependence on precision, Communications in Mathematical Physics, vol. 356, no. 3, pp. 1057-1081, 2017.
G. Wang, Quantum Algorithm for Linear Regression, Physical Review A, vol. 96, p. 012335, 2017.
S. Chakrabarti, Childs, A. M., Li, T., and Wu, X., Quantum algorithms and lower bounds for convex optimization, Quantum, vol. 4, no. 221, 2020.
G. Wang, Quantum Algorithms for Curve Fitting, 2014.
D. Wang, Sundaram, A., Kothari, R., Kapoor, A., and Roetteler, M., Quantum Algorithms for Reinforcement Learning with a Generative Model, Proceedings of the 38th International Conference on Machine Learning, PMLR, vol. 139, 2021.
A. M. Childs, Li, T., Liu, J. - P., Wang, C., and Zhang, R., Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants, Advances in Neural Information Processing Systems (NeurIPS 2022), vol. 35, no. 23205, 2022.
J. D. Watson, Bringewatt, J., Shaw, A. F., Childs, A. M., Gorshkov, A. V., and Davoudi, Z., Quantum Algorithms for Simulating Nuclear Effective Field Theories, 2023.
A. F. Shaw, Lougovski, P., Stryker, J. R., and Wiebe, N., Quantum Algorithms for Simulating the Lattice Schwinger Model, Quantum, vol. 4, no. 306, 2020.
K. Mitra, Strauch, F. W., Lobb, C. J., Anderson, J. R., Wellstood, F. C., and Tiesinga, E., Quantum behavior of the dc SQUID phase qubit, Physical Review B, vol. 77, no. 21, 2008.
B. Augustino, Leng, J., Nannicini, G., Terlaky, T., and Wu, X., A quantum central path algorithm for linear optimization, 2023.
K. Fang, Wang, X., Tomamichel, M., and Berta, M., Quantum Channel Simulation and the Channel's Smooth Max-Information, 2018.
F. Witteveen, Scholz, V., Swingle, B., and Walter, M., Quantum circuit approximations and entanglement renormalization for the Dirac field in 1+1 dimensions, 2019.
F. Witteveen, Scholz, V., Swingle, B., and Walter, M., Quantum circuit approximations and entanglement renormalization for the Dirac field in 1+1 dimensions, 2019.