My research area is quantum information science, an interdisciplinary field at the interface of physics, mathematics and computer science.

My main focus is to use the tool of convex optimization, especially semidefinite programming, to solve problems in quantum information theory. I study the achievability and fundamental limits of quantum correlations and quantum information processing.

My contributions range from areas of quantum resource theory, quantum Shannon theory to the task of channel simulation. I apply tools and techniques from convex optimization to provide efficiently computable approximations for fundamental quantities arose from quantum information theory.
Good examples of my work include: 
(AQIS 2017 long talk, top 10 of all ~140 submissions) 
Non-asymptotic entanglement distillation
Kun Fang
, Xin Wang, Marco Tomamichel, Runyao Duan
It studied the practical scenario of distilling entanglement from finite copies of given states and introduced an efficiently computable framework to estimate the distillation rate via semidefinite programming (SDP).
(QIP 2018 contributed talk) 
Semidefinite programming converse bounds for quantum communication
Xin Wang, Kun Fang, Runyao Duan
It provided improved efficiently computable upper bound for one-shot quantum capacity and asymptotic quantum capacity.
(QIP 2018 contributed talk) 
On converse bounds for classical communication over quantum channels
Xin Wang, Kun Fang, Marco Tomamichel
It provided a finite resource analysis of classical communication over quantum channels, including the first second-order asymptotics beyond entanglement-breaking channels.

(More detailed research statement available upon request)
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