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Xingyao Wu
Xingyao Wu
JD Explore Academy
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Title
Cited by
Cited by
Year
Geometry of the set of quantum correlations
KT Goh, J Kaniewski, E Wolfe, T Vértesi, X Wu, Y Cai, YC Liang, ...
Physical Review A 97 (2), 022104, 2018
862018
Robust self-testing of the three-qubit W state
X Wu, Y Cai, TH Yang, HN Le, JD Bancal, V Scarani
Physical Review A 90 (4), 042339, 2014
712014
Device-independent parallel self-testing of two singlets
X Wu, JD Bancal, M McKague, V Scarani
Physical Review A 93 (6), 062121, 2016
672016
All the self-testings of the singlet for two binary measurements
Y Wang, X Wu, V Scarani
New Journal of Physics 18 (2), 025021, 2016
512016
Machine learning techniques for state recognition and auto-tuning in quantum dots
SS Kalantre, JP Zwolak, S Ragole, X Wu, NM Zimmerman, MD Stewart, ...
npj Quantum Information 5 (1), 1-10, 2019
472019
QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
JP Zwolak, SS Kalantre, X Wu, S Ragole, JM Taylor
PloS one 13 (10), e0205844, 2018
172018
Nonlocal games and optimal steering at the boundary of the quantum set
YZ Zhen, KT Goh, YL Zheng, WF Cao, X Wu, K Chen, V Scarani
Physical Review A 94 (2), 022116, 2016
122016
Exponential improvements for quantum-accessible reinforcement learning
V Dunjko, YK Liu, X Wu, JM Taylor
arXiv preprint arXiv:1710.11160, 2017
102017
Self-testing: Walking on the boundary of the quantum set
X Wu
PQDT-Global, 2016
62016
Maximal tree size of few-qubit states
HN Le, Y Cai, X Wu, R Rabelo, V Scarani
Physical Review A 89 (6), 062333, 2014
32014
Tree-size complexity of multiqubit states
Y Cai, X Wu, V Scarani
Physical Review A 88 (1), 012321, 2013
32013
Quantum circuit architecture search on a superconducting processor
K Linghu, Y Qian, R Wang, MJ Hu, Z Li, X Li, H Xu, J Zhang, T Ma, P Zhao, ...
arXiv preprint arXiv:2201.00934, 2022
12022
The dilemma of quantum neural networks
Y Qian, X Wang, Y Du, X Wu, D Tao
arXiv preprint arXiv:2106.04975, 2021
12021
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning
Q Chen, Y Du, Q Zhao, Y Jiao, X Lu, X Wu
arXiv preprint arXiv:2204.06904, 2022
2022
True machine learning for quantum dot tune-up
J Zwolak, J Taylor, S Kalantre, X Wu
Bulletin of the American Physical Society, 2019
2019
Applying Machine Learning to Quantum-Dot Experiments: Generation of Training Datasets and Auto-tuning
S Kalantre, J Zwolak, X Wu, S Ragole, J Taylor
APS Meeting Abstracts, 2018
2018
Applying Machine Learning to Quantum-Dot Experiments: Learning from the Data
J Zwolak, S Kalantre, X Wu, S Ragole, J Taylor
APS Meeting Abstracts, 2018
2018
A versatile quantum data hiding protocol with enhanced security
X Wu, J Chen, J Taylor
APS Meeting Abstracts, 2018
2018
SELF-TESTING: WALKING ON THE BOUNDARY OF THE QUANTUM SET
WU XINGYAO
2016
Publisher's Note: Nonlocal games and optimal steering at the boundary of the quantum set [Phys. Rev. A 94, 022116 (2016)]
YZ Zhen, KT Goh, YL Zheng, WF Cao, X Wu, K Chen, V Scarani
Physical Review A 94 (3), 039902, 2016
2016
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