Követés
Xingyao Wu
Xingyao Wu
JD Explore Academy
E-mail megerősítve itt: umd.edu - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
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
1542018
Device-independent parallel self-testing of two singlets
X Wu, JD Bancal, M McKague, V Scarani
Physical Review A 93 (6), 062121, 2016
962016
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
932019
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
932014
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
812016
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
762023
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
412018
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
392022
Exponential improvements for quantum-accessible reinforcement learning
V Dunjko, YK Liu, X Wu, JM Taylor
arXiv preprint arXiv:1710.11160, 2017
262017
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
162022
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
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, ...
Entropy 26 (12), 1025, 2024
122024
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning
C Qiuhao, Y Du, Q Zhao, Y Jiao, X Lu, X Wu
Quantum Science and Technology, 2024
102024
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
52014
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, CY Kuo, Y Du, D Tao, X Wu
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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