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M. C. Caro, Huang, H. - Y., Cerezo, M., Sharma, K., Sornborger, A., Cincio, L., and Coles, P. J., “Generalization in quantum machine learning from few training data”, 2021.
J. Carolan and Poremba, A., “Quantum One-Wayness of the Single-Round Sponge with Invertible Permutations”, To appear in the proceedings of CRYPTO 2024, 2024.
A. L. Carter, O’Reilly, J., Toh, G., Saha, S., Shalaev, M., Goetting, I., and Monroe, C., “Ion trap with in-vacuum high numerical aperture imaging for a dual-species modular quantum computer”, Review of Scientific Instruments, vol. 95, 2024.
S. Chakrabarti, Childs, A. M., Li, T., and Wu, X., “Quantum algorithms and lower bounds for convex optimization”, Quantum, vol. 4, no. 221, 2020.
S. Chakrabarti, Krishnakumar, R., Mazzola, G., Stamatopoulos, N., Woerner, S., and Zeng, W. J., “A Threshold for Quantum Advantage in Derivative Pricing”, Quantum, vol. 5, p. 463, 2021.
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, Huang, Y., Li, T., Feizi, S., and Wu, X., “Quantum Wasserstein Generative Adversarial Networks”, Advances in Neural Information Processing Systems (NIPS), vol. 32, 2019.
H. Chan, Katz, J., Nayak, K., Polychroniadou, A., and Shi, E., “More is Less: Perfectly Secure Oblivious Algorithms in the Multi-Server Setting”, 2018.
D. E. Chang, Jiang, L., Gorshkov, A. V., and Kimble, H. J., “Cavity QED with atomic mirrors”, New J. Phys., vol. 14, p. 063003, 2012.
J. Chen, Ji, Z., Li, C. - K., Poon, Y. - T., Shen, Y., Yu, N., Zeng, B., and Zhou, D., “Discontinuity of Maximum Entropy Inference and Quantum Phase Transitions”, New Journal of Physics, vol. 17, no. 8, p. 083019, 2015.
J. Chen and Ying, M., “Ancilla-Assisted Discrimination of Quantum Gates”, 2008.
J. Chen, Chen, X., Duan, R., Ji, Z., and Zeng, B., “No-go Theorem for One-way Quantum Computing on Naturally Occurring Two-level Systems ”, Physical Review A, vol. 83, no. 5, 2011.
J. Chen, Ji, Z., Klyachko, A., Kribs, D. W., and Zeng, B., “Rank Reduction for the Local Consistency Problem”, Journal of Mathematical Physics, vol. 53, no. 2, p. 022202, 2012.
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.
J. Chen, Ji, Z., Kribs, D., Lütkenhaus, N., and Zeng, B., “Symmetric Extension of Two-Qubit States”, Physical Review A, vol. 90, no. 3, 2014.
Y. - A. Chen, Gorshkov, A. V., and Xu, Y., “Error-correcting codes for fermionic quantum simulation”, 2022.
Y. - H. Chen, Chung, K. - M., Lai, C. - Y., Vadhan, S. P., and Wu, X., “Computational Notions of Quantum Min-Entropy”, 2017.
J. Chen, Ji, Z., Ruskai, M. Beth, Zeng, B., and Zhou, D., “Principle of Maximum Entropy and Ground Spaces of Local Hamiltonians”, 2010.
Y. - A. Chen and Hsin, P. - S., “Exactly Solvable Lattice Hamiltonians and Gravitational Anomalies”, 2021.
Y. - A. Chen, Childs, A. M., Hafezi, M., Jiang, Z., Kim, H., and Xu, Y., “Efficient Product Formulas for Commutators and Applications to Quantum Simulation”, Physical Review Research, vol. 4, 2022.
L. Chen, Chen, J., Djokovic, D. Z., and Zeng, B., “Universal Subspaces for Local Unitary Groups of Fermionic Systems”, Communications in Mathematical Physics, vol. 333, no. 2, pp. 541 - 563, 2015.
J. Chen, Dawkins, H., Ji, Z., Johnston, N., Kribs, D., Shultz, F., and Zeng, B., “Uniqueness of Quantum States Compatible with Given Measurement Results”, Physical Review A, vol. 88, no. 1, 2013.
W. Chen, Beck, K. M., Bücker, R., Gullans, M., Lukin, M. D., Tanji-Suzuki, H., and Vuletic, V., “All-Optical Switch and Transistor Gated by One Stored Photon”, Science, vol. 341, no. 6147, pp. 768 - 770, 2013.
J. Chen, Chen, L., and Zeng, B., “Unextendible Product Basis for Fermionic Systems”, Journal of Mathematical Physics, vol. 55, no. 8, p. 082207, 2014.
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.