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Xingyao Wu
Xingyao Wu
JD Explore Academy
Verified email at umd.edu - Homepage
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
1352018
Device-independent parallel self-testing of two singlets
X Wu, JD Bancal, M McKague, V Scarani
Physical Review A 93 (6), 062121, 2016
892016
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
852014
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
832019
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
752016
Recent advances for quantum neural networks in generative learning
J Tian, X Sun, Y Du, S Zhao, Q Liu, K Zhang, W Yi, W Huang, C Wang, ...
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
372023
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
292018
Exponential improvements for quantum-accessible reinforcement learning
V Dunjko, YK Liu, X Wu, JM Taylor
arXiv preprint arXiv:1710.11160, 2017
222017
The dilemma of quantum neural networks
Y Qian, X Wang, Y Du, X Wu, D Tao
IEEE Transactions on Neural Networks and Learning Systems, 2022
142022
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
132016
A distributed learning scheme for variational quantum algorithms
Y Du, Y Qian, X Wu, D Tao
IEEE Transactions on Quantum Engineering 3, 1-16, 2022
122022
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
72022
Self-testing: Walking on the boundary of the quantum set
X Wu
PQDT-Global, 2016
62016
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
42022
Maximal tree size of few-qubit states
HN Le, Y Cai, X Wu, R Rabelo, V Scarani
Physical Review A 89 (6), 062333, 2014
42014
Tree-size complexity of multiqubit states
Y Cai, X Wu, V Scarani
Physical Review A 88 (1), 012321, 2013
3*2013
TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework
Y Chen, X Wu, CY Kuo, Y Du, D Tao
arXiv preprint arXiv:2301.05451, 2023
2023
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
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