Ph.D. in Quantum Information Science, University of Technology Sydney (2015-2018)

Bachelor in Mathematics, Wuhan University (2011-2015)

Professor Kun Fang is a tenure-track assistant professor in the School of Data Science at The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen). Before joining CUHK-Shenzhen, he served as a senior researcher and tech lead at the Institute for Quantum Computing, Baidu, from 2020 to 2023. Prior to that, he worked as a postdoctoral researcher at the University of Cambridge and the University of Waterloo from 2018 to 2020. He earned his PhD in Quantum Information from the University of Technology Sydney in 2018 and obtained his Bachelor’s degree in Mathematics from Wuhan University in 2015.

Professor Fang was a recipient of the Beijing High-level Talents Funding Program (top 30) in 2022 and has been invited to serve as a program committee member for AQIS 2023, a leading conference in quantum information science. To date, he has published over 20 research papers in premier international journals and conferences such as Physical Review Letters, PRX Quantum, IEEE Transactions on Information Theory, Communications in Mathematical Physics, and Mathematical Programming. He has completed over 20 talk presentations at top international conferences, including QIP, TQC, AQIS, ISIT, and ICCOPT. Since 2021, he has filed over 60 patent applications, with 40 of them already granted.

1. Kun Fang and Zi-Wen Liu. No-go theorems for quantum resource purification. Physical Review Letters, 125(6):060405, 2020.

2. Kun Fang and Zi-Wen Liu. No-go theorems for quantum resource purification: new approach and channel theory. PRX Quantum 3, 010337, 2022.

3. Kun Fang, Xin Wang, Marco Tomamichel, and Runyao Duan. Non-asymptotic entanglement distillation. IEEE Transactions on Information Theory, 65(10):6454-6465, 2019.

4. Kun Fang, Xin Wang, Ludovico Lami, Bartosz Regula, and Gerardo Adesso. Probabilistic distillation of quantum coherence. Physical Review Letters, 121(7): 070404, 2018.

5. Kun Fang and Hamza Fawzi. Geometric Rényi divergence and its applications in quantum channel capacities. Communications in Mathematical Physics, 384(3):1615-1677, 2021.

6. Kun Fang, Xin Wang, Marco Tomamichel, and Mario Berta. Quantum channel simulation and the channel’s smooth max-information. IEEE Transactions on Information Theory, 66(4):2129-2140, 2019.

7. Kun Fang, Omar Fawzi, Renato Renner, and David Sutter. Chain rule for the quantum relative entropy. Physical Review Letters, 124(10):100501, 2020.

8. Kun Fang and Hamza Fawzi. The sum-of-squares hierarchy on the sphere and applications in quantum information theory. Mathematical Programming, 190(1): 331-360, 2021.

9. Kun Fang, Jingtian Zhao, Xiufan Li, Yifei Li, Runyao Duan. Quantum NETwork: from theory to practice. SCIENCE CHINA Information Sciences 66: 180509, 2023.

10. Bartosz Regula, Kun Fang, Xin Wang, and Gerardo Adesso. One-shot coherence distillation. Physical Review Letters, 121(1):010401, 2018.

11. Masahito Hayashi, Kun Fang, and Kun Wang. Finite block length analysis on quantum coherence distillation and incoherent randomness extraction. IEEE Transactions on Information Theory, 67(6): 3926-3944, 2021.

12. Xin Wang, Kun Fang, and Marco Tomamichel. On converse bounds for classical communication over quantum channels. IEEE Transactions on Information Theory, 65(7): 4609-4619, 2019.

13. Xin Wang, Kun Fang, and Runyao Duan. Semidefinite programming converse bounds for quantum communication. IEEE Transactions on Information Theory, 65(4): 2583-2592, 2018.

14. Bartosz Regula, Kun Fang, Xin Wang, and Mile Gu. One-shot entanglement distillation beyond local operations and classical communication. New Journal of Physics, 21(10):103017, 2019.

15. María García Díaz, Kun Fang, Xin Wang, Matteo Rosati, Michalis Skotiniotis, John Calsamiglia, and Andreas Winter. Using and reusing coherence to realize quantum processes. Quantum, 2(100), 2018.

A complete list of publications can be found at

https://scholar.google.com.au/citations?user=YlfYNwcAAAAJ&hl=en