I am a tenure-track associate professor at John Hopcroft Center for Computer Science, Shanghai Jiao Tong University. My research focuses on quantum information lying at the intersection of quantum physics and computer science. My research interests include: (1) applying machine learning for quantum information science, and (2) continuous-variable quantum information theory.
Now I have open positions for both postdoc and PhD. Please contact me if you are interested!
News
- Sep 2024 Our paper on applying multi-task neural networks to predict quantum properties of many-body states gets accepted in principle by Nature Communications!
- Aug 2024 My research on applying machine learning to quantum state property prediction and quantum control is supported by the young scientist fund of NSFC!
Working Experience
- 2024-now Tenure-Track Associate Professor, John Center, Shanghai Jiao Tong University (SJTU)
- 2020-2023 Postdoc Fellow, The University of Hong Kong, Supervisor: Prof. Giulio Chiribella
Education
- 2015-2019 Ph.D. in physics, University of Calgary, Supervisor: Prof. Barry Sanders
- 2012-2015 M.Eng. in electronic science and technology, UM-SJTU Joint Institute, SJTU
- 2008-2012 B.Eng. in electronic and computer engineering, UM-SJTU Joint Institute, SJTU
Publications
Machine Learning for Quantum:
- X Gao, M Tian, FX Sun, YD Wu, Y Xiang, Q He, "Classifying Multipartite Continuous Variable Entanglement Structures through Data-augmented Neural Networks", arXiv
- Y Zhu, T Xiao, G Zeng, G Chiribella, YD Wu, "Controlling Unknown Quantum States via Data-Driven State Representations", arXiv
- Y Zhu, YD Wu(co-first), Q Liu, Y Wang, G Chiribella, "Predictive modelling of quantum process with neural networks", arXiv
- YD Wu, Y Zhu, Y Wang, G Chiribella, “Learning quantum properties from short-range correlations using multi-task networks”, Nature Communications 2024 ( "远见"公众号推文)
- YD Wu, Y Zhu, G Bai, Y Wang, G Chiribella, "Quantum Similarity Testing with Convolutional Neural Networks", Physical Review Letters 2023 (highlighted by Nature Computational Science)
- Y Zhu, YD Wu(co-first), G Bai, DS Wang, Y Wang, G Chiribella, "Flexible Learning of Quantum States with Generative Query Neural Networks", Nature Communications 2022
Continuous-Variable Quantum Information:
- YD Wu, Y Zhu, G Chiribella, N Liu, “Efficient learning of continuous-variable quantum states”, Physical Review Research 2024
- YD Wu, G Chiribella, “Detecting quantum capacities of continuous-variable quantum channels”, Physical Review Research 2022
- YD Wu, G Bai, G Chiribella, N Liu, "Efficient Verification of Continuous-Variable Quantum States and Devices without Assuming Identical and Independent Operations", Physical Review Letters 2021
- YD Wu, B Sanders, “Efficient verification of bosonic quantum channels via benchmarking”, New Journal of Physics 2019
- M Ahmadi, YD Wu, B Sanders, “Relativistic (2,3)-threshold quantum secret sharing”, Physical Review D 2017
- YD Wu, J Zhou, X Gong, Y Guo, ZM Zhang, G He, "Continuous-variable measurement-device-independent multipartite communication", Physical Review A 2016
Other topics:
- G Bai, YD Wu, Y Zhu, M Hayashi, G Chiribella, “Quantum causal unravelling”, NPJ quantum information 2022
- C Qian, YD Wu, Y Xiao, B Sanders, "Multiple uncertainty relation for accelerated quantum information", Physical Review D 2020
- M Jafarzadeh, YD Wu(co-first), Y Sanders, B Sanders, “Randomized benchmarking for qudit Clifford gates”, New Journal of Physics 2020
- YD Wu, A Khalid, B Sanders, “Efficient Code for Relativistic Quantum Summoning”, New Journal of Physics 2018