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C
A. M. Childs, Gosset, D., and Webb, Z., Complexity of the XY antiferromagnet at fixed magnetization, Quantum Information and Computation, vol. 16, no. 1-2, pp. 1-18, 2016.
Y. - H. Chen, Chung, K. - M., Lai, C. - Y., Vadhan, S. P., and Wu, X., Computational Notions of Quantum Min-Entropy, 2017.
H. Fu, Wang, D., and Zhao, Q., Computational self-testing of multi-qubit states and measurements, 2022.
C. David White, Cao, C. J., and Swingle, B., Conformal field theories are magical, Physical Review B, vol. 103, no. 7, p. 075145, 2021.
K. - M. Chung, Lee, Y., Lin, H. - H., and Wu, X., Constant-round Blind Classical Verification of Quantum Sampling, 2020.
M. Lemeshko, Yao, N. Y., Gorshkov, A. V., Weimer, H., Bennett, S. D., Momose, T., and Gopalakrishnan, S., Controllable quantum spin glasses with magnetic impurities embedded in quantum solids , Physical Review B, vol. 88, no. 1, 2013.
X. You, Chakrabarti, S., and Wu, X., A Convergence Theory for Over-parameterized Variational Quantum Eigensolvers, 2022.
J. Chen, Ji, Z., Wei, Z., and Zeng, B., Correlations in excited states of local Hamiltonians, Physical Review A, vol. 85, no. 4, 2012.
A. Hu, Mathey, L., Danshita, I., Tiesinga, E., Williams, C. J., and Clark, C. W., Counterflow and paired superfluidity in one-dimensional Bose mixtures in optical lattices , Physical Review A, vol. 80, no. 2, 2009.
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.
G. Pagano, Hess, P. W., Kaplan, H. B., Tan, W. L., Richerme, P., Becker, P., Kyprianidis, A., Zhang, J., Birckelbaw, E., Hernandez, M. R., Wu, Y., and Monroe, C., Cryogenic Trapped-Ion System for Large Scale Quantum Simulation, 2018.
D
Y. Wang, Subhankar, S., Bienias, P., Lacki, M., Tsui, T. - C., Baranov, M. A., Gorshkov, A. V., Zoller, P., Porto, J. V., and Rolston, S. L., Dark state optical lattice with sub-wavelength spatial structure, Phys. Rev. Lett., vol. 120, p. 083601, 2018.
E. - J. Kuo, Seif, A., Lundgren, R., Whitsitt, S., and Hafezi, M., Decoding conformal field theories: from supervised to unsupervised learning, 2021.
S. Debnath, Linke, N. M., Figgatt, C., Landsman, K. A., Wright, K., and Monroe, C., Demonstration of a small programmable quantum computer with atomic qubits, Nature, vol. 536, no. 7614, pp. 63-66, 2016.
A. Hu, Mathey, L., Tiesinga, E., Danshita, I., Williams, C. J., and Clark, C. W., Detecting paired and counterflow superfluidity via dipole oscillations, Physical Review A, vol. 84, no. 4, 2011.
W. Fang, Ying, M., and Wu, X., Differentiable Quantum Programming with Unbounded Loops, 2022.
J. T. Young, Boulier, T., Magnan, E., Goldschmidt, E. A., Wilson, R. M., Rolston, S. L., Porto, J. V., and Gorshkov, A. V., Dissipation induced dipole blockade and anti-blockade in driven Rydberg systems, Phys. Rev. A, vol. 97, p. 023424, 2018.
S. Pabst, Wang, D., and Santra, R., Driving Rabi oscillations at the giant dipole resonance in xenon, Phys. Rev. A , vol. 92, no. 053424, 2015.
E
M. Barbosa, Barthe, G., Fan, X., Grégoire, B., Hung, S. - H., Katz, J., Strub, P. - Y., Wu, X., and Zhou, L., EasyPQC: Verifying Post-Quantum Cryptography, ACM CCS 2021, 2021.
M. Gullans, Thompson, J. D., Wang, Y., Liang, Q. - Y., Vuletic, V., Lukin, M. D., and Gorshkov, A. V., Effective Field Theory for Rydberg Polaritons, Physical Review Letters, vol. 117, no. 11, p. 113601, 2016.
G. Pupillo, Williams, C. J., and Prokof'ev, N. V., Effects of finite temperature on the Mott insulator state, Physical Review A, vol. 73, no. 1, 2006.
Q. Chen, Du, Y., Zhao, Q., Jiao, Y., Lu, X., and Wu, X., Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning, 2022.
E. Onorati, Rouzé, C., França, D. Stilck, and Watson, J. D., Efficient learning of ground & thermal states within phases of matter, 2023.
D. An, Fang, D., Jordan, S., Liu, J. - P., Low, G. Hao, and Wang, J., Efficient quantum algorithm for nonlinear reaction-diffusion equations and energy estimation, 2022.
G. Wang, Efficient quantum algorithms for analyzing large sparse electrical networks, Quantum Information & Computation, vol. 17, no. 11&12, pp. 987-1026, 2017.