BIMSA > Song Cheng
Assistant Professor Song Cheng

Group: Quantum Symmetry

Office: A3-3a-302

Email: chengsong@bimsa.cn

Research Field: Tensor Network

Biography

Song Cheng is an Assistant Professor at the Yanqi Lake Beijing Institute of Mathematical Sciences and Applications (BIMSA). He holds a PhD in theoretical physics from the Institute of Physics, CAS, and previously served as an Assistant Professor at the Center of Quantum Computing in Pengcheng Laboratory. His current research focuses on investigating the relationship between machine learning, quantum many-body physics, and quantum computing through tensor networks.

Research Interest

  • My research interests are focused on novel applications of tensor network algorithms in the fields of machine learning and quantum computing, which includes classical and quantum machine learning algorithms based on tensor networks and classical simulation algorithms of noisy quantum circuits based on tensor networks.I am also interested in the theoretical aspects of tensor networks and their relationship with topological states of quantum matter, as well as their connections with quantum error correction and quantum codes. This includes the representation of topological quantum states using tensor networks and the numericalization of mathematical concepts in topological quantum field theory.

Education Experience

  • 2014 - 2019      Institute of Physics, CAS      Theoretical Physics      Doctor      (Supervisor: Prof. Tao Xiang and Lei Wang))
  • 2010 - 2014      Sichuan University      Physics      Bachelor

Work Experience

  • 2020 -      Quantum information group, BIMSA      Assistant Research Fellow
  • 2019 - 2020      Quantum Machine Learning Group, Quantum Computing Center, Pengcheng laboratory, Shenzhen      Assistant Professor

Honors and Awards

  • 2024      若琳论文奖

Publication

  • [1] Ruiqi Zhang, Yuguo Shao, Fuchuan Wei, Song Cheng, Zhaohui Wei, Zhengwei Liu, Clifford Perturbation Approximation for Quantum Error Mitigation, arXiv:2412.09518 (2024)
  • [2] Yuguo Shao, Fuchuan Wei, Song Cheng, and Zhengwei Liu, Simulating Noisy Variational Quantum Algorithms: A Polynomial Approach, PHYSICAL REVIEW LETTERS, 133, 120603 (2024)
  • [3] Y Shao, F Wei, S Cheng, Z Liu, Simulating Quantum Mean Values in Noisy Variational Quantum Algorithms: A Polynomial-Scale Approach, arXiv:2306.05804(2023)
  • [4] Z. P. Wu, S. Cheng*, B, Zeng, A ZX-Calculus Approach to Concatenated Graph Codes, arXiv: 2304.08363(2023)
  • [5] S. Cheng, Chenfeng Cao, Chao Zhang, Yongxiang Liu, Shi-Yao Hou, Pengxiang Xu,Bei Zeng., Simulating Noisy Quantum Circuits with Matrix Product Density Operators., Physical Review Research(2021)
  • [6] Song Cheng, Lei Wang, and Pan Zhang, Supervised learning with projected entangled pair states, Physical Review B, 103(2021), 125117
  • [7] Ze-Feng Gao, Song Cheng, Rong-Qiang He, Z. Y. Xie, Hui-Hai Zhao, Zhong-Yi Lu, and Tao Xiang, Compressing deep neural networks by matrix product operators, Physical Review Research, 2(2020), 2, 023300
  • [8] Song Cheng, Lei Wang, Tao Xiang, and Pan Zhang, Tree Tensor Networks for Generative Modeling., Physical Review B, 99(2019), 155131
  • [9] Song Cheng, Jing Chen, and Lei Wang, Information perspective to probabilistic modeling: Boltzmann machines versus born machines.(2018)
  • [10] Jing Chen, Song Cheng, Haidong Xie, Lei Wang, and Tao Xiang, Equivalence of restricted Boltzmann machines and tensor network states.(2018)
  • [11] 程嵩, 陈靖, 王磊, 量子纠缠：从量子物质态到深度学习(2017)
  • [12] Phase Transition of the q-State Clock Model: Duality and Tensor Renormalization. Chen, J., Liao, H.-J., Xie, H.-D., Han, X.-J., Huang, R.-Z., Cheng, S., et al. 2017, Chinese Physics Letters, 34, 050503.

 

Update Time: 2025-05-20 15:34:25