Xin Wang

Xin Wang

Associate Professor



I am an Associate Professor at the Thrust of Artificial Intelligence, Information Hub, Hong Kong University of Science and Technology (Guangzhou), China. My research investigates a broad range of perspectives of quantum information science, including quantum Shannon theory, quantum resource theory, quantum machine learning, quantum algorithms, quantum error processing, and quantum software. I am also an editor of Quantum.

Previously, I was a Staff Researcher at the Institute for Quantum Computing at Baidu Research, where I focused on the research on quantum computing and the development of Baidu Quantum Platform. In particular, I led the development of Paddle Quantum, a Python library for quantum machine learning. From 2018 to 2019, I was a Hartree Postdoctoral Fellow at the Joint Center for Quantum Information and Computer Science (QuICS) at the University of Maryland, College Park. I received my doctorate in quantum information from the University of Technology Sydney in 2018, under the supervision of Prof. Runyao Duan and Prof. Andreas Winter. I obtained my B.S. in mathematics (with Wu Yuzhang Honor) from Sichuan University in 2014.

I am a recipient of National Young Talents Project, Top Young Chinese Scholars in Artificial Intelligence (AI+X), Chancellor’s List for Outstanding Thesis of UTS, and Outstanding Self-financed Overseas Student Award. I was in the list of The World’s Top 2% Scientists 2023 (published by Stanford University).

A full list of my publications can be found on Google Scholar or arXiv. My full CV is available here.

Hiring: I am looking for self-motivated students (PhD, MPhil, research assistant, intern) and postdoctoral scholars interested in quantum information, quantum machine learning, and quantum computing. Check out the page of recruitment for details. If you are interested in joining the journey from the fundamentals of quantum information to the frontier of Quantum AI, please feel free to contact!

Update: The website of my group is online.

  • Quantum Information Theory
  • Quantum Computation
  • Quantum Machine Learning
  • Quantum Error Mitigation and Correction
  • Quantum Simulation
  • Quantum Software and Architecture
  • PhD in Quantum Information, 2018

    University of Technology Sydney

  • BSc in Mathematics, 2014

    Sichuan University


Associate Professor
Jun 2023 – Present Guangzhou, China
Principal investigator on quantum AI and quantum information.
Staff Researcher and Tech Leader
Jul 2019 – May 2023 Beijing, China
Research on quantum machine learning and platform development.
Hartree Fellow
Aug 2018 – Jun 2019 Maryland, USA
Research on quantum entanglement, fault-tolerent quantum computing, quantum simulation.


Recent Publications

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(2023). Upper Bounds on the Distillable Randomness of Bipartite Quantum States. arXiv:2212.09073.

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(2023). Bounding the forward classical capacity of bipartite quantum channels. IEEE Transactions on Information Theory.


(2023). Lower bound the T-count via unitary stabilizer nullity. Physical Review Applied.

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(2023). Optimal Quantum Dataset for Learning a Unitary Transformation. Physical Review Applied.