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Journal Article
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.
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.
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.
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.
E. Tiesinga and Johnson, P. R., “Quadrature interferometry for nonequilibrium ultracold bosons in optical lattices ”, Physical Review A, vol. 87, no. 1, 2013.
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.
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. Kimmel, “Quantum Adversary (Upper) Bound”, Chicago Journal of Theoretical Computer Science, vol. 19, no. 1, pp. 1 - 14, 2013.
A. M. Childs and van Dam, W., “Quantum algorithm for a generalized hidden shift problem”, 2005.
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.
D. An, Childs, A. M., and Lin, L., “Quantum algorithm for linear non-unitary dynamics with near-optimal dependence on all parameters”, 2023.
G. Wang, “Quantum Algorithm for Linear Regression”, Physical Review A, vol. 96, p. 012335, 2017.
J. Chen, Childs, A. M., and Hung, S. - H., “Quantum algorithm for multivariate polynomial interpolation”, Proceedings of The Royal Society A, vol. 474, no. 2209, 2018.
P. C. S. Costa, Jordan, S. P., and Ostrander, A., “Quantum Algorithm for Simulating the Wave Equation”, Phys. Rev. A , vol. 99 , no. 012323 , 2019.
A. M. Childs, Kothari, R., and Somma, R. D., “Quantum algorithm for systems of linear equations with exponentially improved dependence on precision”, SIAM Journal on Computing, vol. 46, no. 6, pp. 1920-1950, 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.
A. M. Childs, Coudron, M., and Gilani, A. Shiraz, “Quantum Algorithms and the Power of Forgetting”, 14th Innovations in Theoretical Computer Science Conference (ITCS 2023), vol. 251, p. 37:1--37:22, 2023.
A. M. Childs and van Dam, W., “Quantum algorithms for algebraic problems”, Reviews of Modern Physics, vol. 82, no. 1, pp. 1 - 52, 2010.
G. Wang, “Quantum Algorithms for Curve Fitting”, 2014.
C. Zhang, Leng, J., and Li, T., “Quantum Algorithms for Escaping from Saddle Points”, Quantum, vol. 5, no. 529, 2021.