1. Research
  2. Publications

Publications

Export 1257 results:
Author [ Title(Desc)] Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
L
K. C. Cox, Bienias, P., Meyer, D. H., Fahey, D. P., Kunz, P. D., and Gorshkov, A. V., “Linear and continuous variable spin-wave processing using a cavity-coupled atomic ensemble”, 2021.
D. An, Liu, J. - P., and Lin, L., “Linear combination of Hamiltonian simulation for non-unitary dynamics with optimal state preparation cost”, Phys. Rev. Lett., vol. 131, no. 150603, 2023.
J. Haferkamp, Faist, P., Kothakonda, N. B. T., Eisert, J., and Halpern, N. Yunger, “Linear growth of quantum circuit complexity”, Nat. Phys., 2022.
Y. - K. Liu, “The Local Consistency Problem for Stoquastic and 1-D Quantum Systems”, 2007.
N. J. Coble, Coudron, M., Nelson, J., and Nezhadi, S. Sajjad, “Local Hamiltonians with no low-energy stabilizer states”, 2023.
H. Fu and Miller, C., “Local randomness: Examples and application”, Phys. Rev. A, no. 97, p. 032324, 2018.
M. C. Tran, Guo, A. Y., Su, Y., Garrison, J. R., Eldredge, Z., Foss-Feig, M., Childs, A. M., and Gorshkov, A. V., “Locality and digital quantum simulation of power-law interactions”, Phys. Rev. X 9, 031006, vol. 9, no. 031006, 2019.
M. C. Tran, Ehrenberg, A., Guo, A. Y., Titum, P., Abanin, D. A., and Gorshkov, A. V., “Locality and Heating in Periodically Driven, Power-law Interacting Systems”, Phys. Rev. A , vol. 100, no. 052103, 2019.
S. S. Bullock and O'Leary, D. P., “Locality Bounds on Hamiltonians for Stabilizer Codes”, Quantum Information and Computation, vol. 9, 2009.
S. Xu and Swingle, B., “Locality, Quantum Fluctuations, and Scrambling”, Phys. Rev. X , vol. 9, no. 031048, 2019.
F. Liu, Yang, Z. - C., Bienias, P., Iadecola, T., and Gorshkov, A. V., “Localization and criticality in antiblockaded 2D Rydberg atom arrays”, 2020.
K. Klocke, White, C. David, and Buchhold, M., “Localization crossover and subdiffusive transport in a classical facilitated network model of a disordered, interacting quantum spin chain”, 2021.
D. Bluvstein, Evered, S. J., Geim, A. A., Li, S. H., Zhou, H., Manovitz, T., Ebadi, S., Cain, M., Kalinowski, M., Hangleiter, D., J. Ataides, P. Bonilla, Maskara, N., Cong, I., Gao, X., Rodriguez, P. Sales, Karolyshyn, T., Semeghini, G., Gullans, M., Greiner, M., Vuletic, V., and Lukin, M. D., “Logical quantum processor based on reconfigurable atom arrays”, Nature, 2023.
Z. Davoudi, Detmold, W., Fu, Z., Grebe, A. V., Jay, W., Murphy, D., Oare, P., Shanahan, P. E., and Wagman, M. L., “Long-Distance Nuclear Matrix Elements for Neutrinoless Double-Beta Decay from Lattice QCD”, 2024.
A. Chotia, Neyenhuis, B., Moses, S. A., Yan, B., Covey, J. P., Foss-Feig, M., Rey, A. Maria, Jin, D. S., and Ye, J., “Long-lived dipolar molecules and Feshbach molecules in a 3D optical lattice ”, Physical Review Letters, vol. 108, no. 8, 2012.
J. M. Taylor, Marcus, C. M., and Lukin, M. D., “Long-lived memory for mesoscopic quantum bits”, Physical Review Letters, vol. 90, no. 20, 2003.
L. Pedro García-Pintos, Brady, L. T., Bringewatt, J., and Liu, Y. - K., “Lower Bounds on Quantum Annealing Times”, Phys. Rev. Lett., vol. 130, no. 140601, 2023.
A. M. Childs, Haselgrove, H. L., and Nielsen, M. A., “Lower bounds on the complexity of simulating quantum gates”, Physical Review A, vol. 68, no. 5, 2003.
Z. Ji, Chen, J., Wei, Z., and Ying, M., “The LU-LC conjecture is false”, 2007.
M
A. Seif, Landsman, K. A., Linke, N. M., Figgatt, C., Monroe, C., and Hafezi, M., “Machine learning assisted readout of trapped-ion qubits”, J. Phys. B: At. Mol. Opt. Phys., vol. 51, 2018.
W. Zhong, Gold, J. M., Marzen, S., England, J. L., and Halpern, N. Yunger, “Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive”, Scientific Reports, vol. 11, 2021.
S. S. Kalantre, Zwolak, J. P., Ragole, S., Wu, X., Zimmerman, N. M., Stewart, M. D., and Taylor, J. M., “Machine Learning techniques for state recognition and auto-tuning in quantum dots”, 2017.
A. Seif, Hafezi, M., and Jarzynski, C., “Machine learning the thermodynamic arrow of time”, Nat. Phys., pp. 1-9, 2020.
T. Zolkin, Kharkov, Y., and Nagaitsev, S., “Machine-assisted discovery of integrable symplectic mappings”, 2022.
S. Guo, Fritsch, A. R., Greenberg, C., Spielman, I. B., and Zwolak, J. P., “Machine-learning enhanced dark soliton detection in Bose-Einstein condensates”, Mach. Learn.: Sci. Technol. , vol. 2, p. 035020, 2021.