| 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 | 146 | 2018 |
| Device-independent parallel self-testing of two singlets X Wu, JD Bancal, M McKague, V Scarani Physical Review A 93 (6), 062121, 2016 | 94 | 2016 |
| 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 | 89 | 2014 |
| 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 | 87 | 2019 |
| 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 | 83 | 2016 |
| 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 | 56 | 2023 |
| 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 | 35 | 2018 |
| Exponential improvements for quantum-accessible reinforcement learning V Dunjko, YK Liu, X Wu, JM Taylor arXiv preprint arXiv:1710.11160, 2017 | 22 | 2017 |
| 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 | 19 | 2022 |
| 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 | 14 | 2022 |
| 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 | 13 | 2016 |
| 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 | 10 | 2022 |
| 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 | 6 | 2022 |
| Self-testing: Walking on the boundary of the quantum set X Wu PQDT-Global, 2016 | 6 | 2016 |
| Maximal tree size of few-qubit states HN Le, Y Cai, X Wu, R Rabelo, V Scarani Physical Review A 89 (6), 062333, 2014 | 5 | 2014 |
| Tree-size complexity of multiqubit states Y Cai, X Wu, V Scarani Physical Review A 88 (1), 012321, 2013 | 3* | 2013 |
| 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 | | 2024 |
| 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 |
Valerio ScaraniCentre for Quantum Technologies, National University of SingaporeVerified email at nus.edu.sg
