Xingyao Wu

xingyao-wu's picture
Postdoctoral Scholar (2017-2019)
xywu@umd.edu
3100N Atlantic Building
(301) 314-1844

Xingyao Wu's research focuses on quantum information and quantum computation. Recently, he has been interested in novel physical systems that are suitable for implementing quantum information processing and computation. Another aspect of his study is the possible benefits one may get by exploiting the properties of quantum resources, for instance entanglement, in different information tasks, especially in the multipartite cases. Wu received his doctorate from Centre for Quantum Technologies, National University of Singapore in February 2017. 

Publications

2018

K. Tong Goh, Kaniewski, J., Wolfe, E., Vértesi, T., Wu, X., Cai, Y., Liang, Y. - C., and Scarani, V., “Geometry of the quantum set of correlations”, Physical Review A, vol. 97, no. 2, p. 022104, 2018.
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.
X. Wu and Chen, J., “Multiparty quantum data hiding with enhanced security and remote deletion”, p. 5, 2018.

2017

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.
V. Dunjko, Liu, Y. - K., Wu, X., and Taylor, J. M., “Super-polynomial and exponential improvements for quantum-enhanced reinforcement learning”, 2017.
V. Dunjko, Liu, Y. - K., Wu, X., and Taylor, J. M., “Exponential improvements for quantum-accessible reinforcement learning”, 2017.