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2022
D. Hangleiter, Carolan, J., and Thébault, K. P. Y., Analogue Quantum Simulation: A New Instrument for Scientific Understanding. Springer Nature, 2022, pp. 83-102.
H. Fu, Constant-sized correlations are sufficient to robustly self-test maximally entangled states with unbounded dimension, Quantum, vol. 6, p. 614, 2022.
D. H. Wu and Albert, V. V., Approximating the two-mode two-photon Rabi model, Physics Letters A, vol. 422, 2022.
N. Yunger Halpern and Majidy, S., How to build Hamiltonians that transport noncommuting charges in quantum thermodynamics, npj Quantum Inf, vol. 8, no. 10, 2022.
B. J. Weber, Kalantre, S. S., McJunkin, T., Taylor, J. M., and Zwolak, J. P., Theoretical bounds on data requirements for the ray-based classification, SN Comput. Sci., vol. 3, no. 57, 2022.
J. Ziegler, McJunkin, T., Joseph, E. S., Kalantre, S. S., Harpt, B., Savage, D. E., Lagally, M. G., Eriksson, M. A., Taylor, J. M., and Zwolak, J. P., Toward Robust Autotuning of Noisy Quantum dot Devices, Physical Review Applied, vol. 17, 2022.
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.
C. J. Cao and Lackey, B., Quantum Lego: Building Quantum Error Correction Codes from Tensor Networks, PRX Quantum, vol. 3, no. 2, p. 020332, 2022.
A. R. Fritsch, Guo, S., Koh, S. M., Spielman, I. B., and Zwolak, J. P., Dark Solitons in Bose-Einstein Condensates: A Dataset for Many-body Physics Research, 2022.
S. Guo, Koh, S. M., Fritsch, A. R., Spielman, I. B., and Zwolak, J. P., Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons, Phys. Rev. Research, vol. 4, p. 023163 , 2022.
J. Bringewatt and Brady, L. T., Simultaneous Stoquasticity, Phys. Rev. A, vol. 105, no. 062601, 2022.
Z. A. Benson, Peshkov, A., Halpern, N. Yunger, Richardson, D. C., and Losert, W., Experimentally Measuring Rolling and Sliding in Three-Dimensional Dense Granular Packings, Phys. Rev. Lett., vol. 129, no. 4, p. 048001, 2022.
D. Wu, Zhao, Q., Wang, C., Huang, L., Jiang, Y. - F., Bai, B., Zhou, Y., Gu, X. - M., Liu, F. - M., Mao, Y. - Q., Sun, Q. - C., Chen, M. - C., Zhang, J., Peng, C. - Z., Zhu, X. - B., Zhang, Q., Lu, C. - Y., and Pan, J. - W., Closing the Locality and Detection Loopholes in Multiparticle Entanglement Self-Testing, Physical Review Letters, vol. 128, no. 25, p. 250401, 2022.
T. Sewell, Bapat, A., and Jordan, S., Estimating gate complexities for the site-by-site preparation of fermionic vacua, 2022.
M. Van Regemortel, Shtanko, O., García-Pintos, L. Pedro, Deshpande, A., Dehghani, H., Gorshkov, A. V., and Hafezi, M., Monitoring-induced Entanglement Entropy and Sampling Complexity, 2022.
H. Fu, Wang, D., and Zhao, Q., Computational self-testing of multi-qubit states and measurements, 2022.
A. Deshpande, Mehta, A., Vincent, T., Quesada, N., Hinsche, M., Ioannou, M., Madsen, L., Lavoie, J., Qi, H., Eisert, J., Hangleiter, D., Fefferman, B., and Dhand, I., Quantum computational advantage via high-dimensional Gaussian boson sampling, Science Advances, vol. 8, p. eabi7894, 2022.
M. - C. Hsu, Kuo, E. - J., Yu, W. - H., Cai, J. - F., and Hsieh, M. - H., Quantum state tomography via non-convex Riemannian gradient descent, 2022.
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.
A. Singh Arora, Coladangelo, A., Coudron, M., Gheorghiu, A., Singh, U., and Waldner, H., Quantum Depth in the Random Oracle Model, 2022.