{\rtf1\ansi\deff0\deftab360 {\fonttbl {\f0\fswiss\fcharset0 Arial} {\f1\froman\fcharset0 Times New Roman} {\f2\fswiss\fcharset0 Verdana} {\f3\froman\fcharset2 Symbol} } {\colortbl; \red0\green0\blue0; } {\info {\author Biblio 7.x}{\operator }{\title Biblio RTF Export}} \f1\fs24 \paperw11907\paperh16839 \pgncont\pgndec\pgnstarts1\pgnrestart K. Tong Goh, Kaniewski, J., Wolfe, E., V\'e9rtesi, 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.\par \par X. Wu and Chen, J., ?Multiparty quantum data hiding with enhanced security and remote deletion?, p. 5, 2018.\par \par 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.\par \par V. Dunjko, Liu, Y. - K., Wu, X., and Taylor, J. M., ?Exponential improvements for quantum-accessible reinforcement learning?, 2017.\par \par 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.\par \par V. Dunjko, Liu, Y. - K., Wu, X., and Taylor, J. M., ?Super-polynomial and exponential improvements for quantum-enhanced reinforcement learning?, 2017.\par \par }