Sirui Lu

Master student

Tel (+ 49 89) 3 29 05 - 504
Room: A 0.29
Max-Planck-Institute of Quantum Optics
Hans-Kopfermann-Str. 1
85748 Garching, Germany
Sirui Lu


I received my bachelor's degree in science from the department of physics, Tsinghua University. My research focuses on theoretical quantum information science. Since the spring of 2017, I have been working on many-body physics, quantum algorithms and quantum optics under the supervision of Prof. Luming Duan. In the summer of 2016, I started working in Prof. Bei Zeng’s theoretical quantum information group, where I have investigated quantum error correction, entanglement, and state tomography. During my undergraduate study, I have also spent an exchanging year at MIT and carried out a summer research project at Harvard.

Research Interests

My research mainly focuses on theoretical quantum information science, with emphasis on the following topics:
  • Understanding many-body physics with tools from quantum information such as tensor network and entanglement.
  • Quantum algorithms on near-term devices: quantum machine learning and quantum approximate optimization algorithms (QAOA).
  • Applying machine learning techniques to solve complex problems in quantum physics.
  • Quantum error correction and fault tolerance.

  • Selected Papers

  • Murphy Yuezhen Niu, Sirui Lu, Issac L. Chuang. Optimizing QAOA: Success Probability and Runtime Dependence on Circuit Depth. arXiv:quant-ph/1905.12134, 2019.
  • W.-Q. Lian∗, S.-T. Wang∗, S.-R. Lu, Y.-Y. Huang, F. Wang, X.-X. Yuan, W.-G. Zhang, X.-L. Ouyang, X. Wang, X.-Z. Huang, L. He, X.-Y. Chang, D.-L. Deng, and L.-M. Duan. Machine learning topological phases with a solid-state quantum simulator. Phys. Rev. Lett. 122, 210503, 2019.
  • Sirui Lu, Xun Gao, L.-M. Duan. Efficient representation of topologically ordered states with restricted Boltzmann machines. Phys. Rev. B, 99, 155136, 2019.
  • Sirui Lu*, Shilin Huang*, Keren Li, Jun Li, Jianxin Chen, Dawei Lu, Zhengfeng Ji, Yi Shen, Duanlu Zhou, and Bei Zeng. Separability-entanglement classifier via machine learning. Phys. Rev. A, 98:012315, Jul 2018.
  • Markus Grassl, Sirui Lu, and Bei Zeng. Codes for simultaneous transmission of quantum and classical information. IEEE International Symposium on Information Theory Proceedings (ISIT), 2017, pages 1718–1722. IEEE, 2017.


    Please find my information about me at my homepage:

    My publications on the arXiv Google Schoolar