Publications

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Q
M. Jarret, Lackey, B., Liu, A., and Wan, K., Quantum adiabatic optimization without heuristics, 2018.
L. Li, Chang, L., Cleaveland, R., Zhu, M., and Wu, X., The Quantum Abstract Machine, 2024.
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.
X. Wang, Wilde, M. M., and Su, Y., Quantifying the magic of quantum channels, New Journal of Physics, vol. 21, no. 103002, 2019.
J. Chen, Johnston, N., Li, C. - K., and Plosker, S., Quantifying the coherence of pure quantum states, Physical Review A, vol. 94, no. 4, p. 042313, 2016.
E. Tiesinga and Johnson, P. R., Quadrature interferometry for nonequilibrium ultracold bosons in optical lattices , Physical Review A, vol. 87, no. 1, 2013.
M. J. O'Hara and O'Leary, D. P., Quadratic fermionic interactions yield effective Hamiltonians for adiabatic quantum computing , Physical Review A, vol. 79, no. 3, 2009.
D. P. O'Leary and Bullock, S. S., QR Factorizations Using a Restricted Set of Rotations, Electronic Transactions on Numerical Analysis, vol. 21, pp. 20-27, 2005.
S. P. Jordan, Gosset, D., and Love, P. J., QMA-complete problems for stoquastic Hamiltonians and Markov matrices, Physical Review A, vol. 81, no. 3, 2010.
S. Kushnir, Leng, J., Peng, Y., Fan, L., and Wu, X., QHDOPT: A Software for Nonlinear Optimization with Quantum Hamiltonian Descent, 2024.
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.
L. Li, Zhu, M., Cleaveland, R., Lee, Y., Chang, L., and Wu, X., Qafny: Quantum Program Verification Through Type-guided Classical Separation Logic, 2023.
L. Li, Zhu, M., Cleaveland, R., Nicolellis, A., Lee, Y., Chang, L., and Wu, X., Qafny: A Quantum-Program Verifier, 2024.
P
X. Ma, Jackson, T., Zhou, H., Chen, J., Lu, D., Mazurek, M. D., Fisher, K. A. G., Peng, X., Kribs, D., Resch, K. J., Ji, Z., Zeng, B., and Laflamme, R., Pure-state tomography with the expectation value of Pauli operators, Physical Review A, vol. 93, no. 3, p. 032140, 2016.
Z. Ji, Liu, Y. - K., and Song, F., Pseudorandom States, Non-Cloning Theorems and Quantum Money, In: Shacham H., Boldyreva A. (eds) Advances in Cryptology – CRYPTO 2018. CRYPTO 2018. Lecture Notes in Computer Science., vol. 10993, 2018.
B. Fefferman and Umans, C., Pseudorandom generators and the BQP vs. PH problem, 2010.
G. Pupillo, Rey, A. Maria, Williams, C. J., and Clark, C. W., Pseudo-fermionization of 1-D bosons in optical lattices, New Journal of Physics, vol. 8, no. 8, pp. 161 - 161, 2006.
K. Hietala, Rand, R., Hung, S. - H., Li, L., and Hicks, M., Proving Quantum Programs Correct, 12th International Conference on Interactive Theorem Proving (ITP 2021), vol. 193, pp. 21:1–21:19, 2021.
K. Hietala, Rand, R., Hung, S. - H., Li, L., and Hicks, M., Proving Quantum Programs Correct, Schloss Dagstuhl, 2021.
H. - Y. Huang, Kueng, R., Torlai, G., Albert, V. V., and Preskill, J., Provably efficient machine learning for quantum many-body problems, Science, vol. 377, 2022.
E. Onorati, Rouzé, C., França, D. Stilck, and Watson, J. D., Provably Efficient Learning of Phases of Matter via Dissipative Evolutions, 2023.
Y. Tong, Albert, V. V., McClean, J. R., Preskill, J., and Su, Y., Provably accurate simulation of gauge theories and bosonic systems, Quantum, vol. 6, p. 816, 2022.
A. Kyrillidis, Kalev, A., Park, D., Bhojanapalli, S., Caramanis, C., and Sanghavi, S., Provable quantum state tomography via non-convex methods, 2017.
J. Bringewatt, Boettcher, I., Niroula, P., Bienias, P., and Gorshkov, A. V., Protocols for estimating multiple functions with quantum sensor networks: Geometry and performance, Physical Review Research, vol. 3, 2021.
E. Waks and Monroe, C., Protocol for Hybrid Entanglement Between a Trapped Atom and a Semiconductor Quantum Dot , Physical Review A, vol. 80, no. 6, 2009.