BIMSA > 程嵩
助理研究员 程嵩

团队: 量子对称

办公室: A3-3a-302

邮箱: chengsong@bimsa.cn

研究方向: 张量网络

个人简介

程嵩，现任北京雁栖湖应用数学研究院助理研究员，曾任鹏城实验室量子计算中心助理研究员，博士毕业于中科院物理所理论物理专业。他的研究方向是张量网络算法，研究兴趣主要集中于开发张量网络在凝聚态物理，机器学习，量子计算等方向的新算法。

研究兴趣

  • 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.

教育经历

  • 2014 - 2019      中国科学院物理研究所      理论物理      博士      (Supervisor: Prof. Tao Xiang and Lei Wang))
  • 2010 - 2014      四川大学      物理      学士

工作经历

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

荣誉与奖项

  • 2024      若琳论文奖

出版物

  • [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.

 

更新时间: 2025-05-07 17:03:51