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Xingyao Wu
Xingyao Wu
JD Explore Academy
Bestätigte E-Mail-Adresse bei umd.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
1342018
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
862014
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
782019
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
272023
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
262018
Exponential improvements for quantum-accessible reinforcement learning
V Dunjko, YK Liu, X Wu, JM Taylor
arXiv preprint arXiv:1710.11160, 2017
212017
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
132022
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
112022
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
62022
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
42014
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
32022
Tree-size complexity of multiqubit states
Y Cai, X Wu, V Scarani
Physical Review A 88 (1), 012321, 2013
32013
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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