BIMSA > 刘锦鹏
助理教授 刘锦鹏

单位: 清华丘成桐数学科学中心, 北京雁栖湖应用数学研究院

团队: 量子对称

办公室: A15-208

邮箱: liujinpeng@bimsa.cn

研究方向: Quantum Computation

个人主页: https://www.jin-peng-liu.me/

个人简介

刘锦鹏，清华大学丘成桐数学科学中心助理教授。他曾于 2022 年至 2024 年在麻省理工和伯克利担任西蒙斯量子博士后研究员，于 2022 年获得马里兰大学博士学位。刘锦鹏的主要研究领域为量子模拟算法，量子科学计算，量子机器学习等。他开创性地发展了一系列量子算法用于求解微分方程、采样与优化问题，并解决了量子计算领域15年的公开猜想：提出首个多项式时间求解非线性微分方程的量子算法。他在PNAS、Nat. Commun.、PRL、CMP、JCP、Quantum 等期刊和 NeurIPS、QIP、TQC等会议发表论文多篇，并受到 Quanta、SIAM News、MATH+ 等科技媒体报道。他曾获得 ICCM 毕业论文奖（博士论文金奖）、NSF Robust Quantum Simulation Seed Grant （共同PI）、NSF QISE-NET Triplet Award、James C. Alexander Prize等荣誉。刘锦鹏现为量子信息领域顶刊Quantum的编委，是中国高校现有的3名编委之一。

研究兴趣

  • 量子模拟算法
  • 量子科学计算
  • 量子机器学习

教育经历

  • 2017 - 2022      马里兰大学      应用数学与科学计算      理学博士      (Supervisor: Andrew Childs)
  • 2013 - 2017      北京航空航天大学-中科院华罗庚班      数学（华罗庚班）      理学学士      (Supervisor: 袁亚湘)

工作经历

  • 2024 -      清华大学丘成桐数学科学中心      助理教授
  • 2023 - 2024      麻省理工学院      博士后研究员
  • 2022 - 2023      加州大学伯克利分校      西蒙斯量子博士后研究员

荣誉与奖项

  • 2024      ICCM毕业论文奖（博士论文金奖）
  • 2023      NSF Robust Quantum Simulation Seed Grant（共同PI）
  • 2023      James C. Alexander Prize
  • 2021      NSF QISE-NET Triplet Award

出版物

  • [1] Zhenning Liu, Xiantao Li, Chunhao Wang, Jin-Peng Liu, Toward end-to-end quantum simulation for protein dynamics, arXiv:2411.03972 (2024)
  • [2] P Schleich, T Kharazi, X Li, JP Liu, A Aspuru-Guzik, N Wiebe, Arbitrary Boundary Conditions and Constraints in Quantum Algorithms for Differential Equations via Penalty Projections, arXiv (2025)
  • [3] F Wei, R Lu, Y Shao, J Li, JP Liu, Z Liu, BELT: Block Encoding of Linear Transformation on Density Matrices, arXiv (2025)
  • [4] R Lu, HE Li, Z Liu, JP Liu, Infinite-dimensional Extension of the Linear Combination of Hamiltonian Simulation: Theorems and Applications, arXiv (2025)
  • [5] D An, JP Liu, D Wang, Q Zhao, Quantum Differential Equation Solvers: Limitations and Fast-Forwarding: D. An, J.-P. Liu, D. Wang, Q. Zhao, Communications in Mathematical Physics, 406(8), 189 (2025)
  • [6] Y Wang, R Jiang, Y Fan, X Jia, J Eisert, J Liu, JP Liu, Towards efficient quantum algorithms for diffusion probability models, arXiv (2025)
  • [7] T Kharazi, AM Alkadri, JP Liu, KK Mandadapu, KB Whaley, Explicit block encodings of boundary value problems for many-body elliptic operators, Quantum, 9, 1764 (2025)
  • [8] C Peng, JP Liu, GW Chern, D Luo, Provably efficient adiabatic learning for quantum-classical dynamics, arXiv (2024)
  • [9] J Liu, M Liu, JP Liu, Z Ye, Y Wang, Y Alexeev, J Eisert, L Jiang, Towards provably efficient quantum algorithms for large-scale machine-learning models, Nature Communications, 15(1), 434 (2024)
  • [10] JP Liu, L Lin, Dense outputs from quantum simulations, Journal of Computational Physics, 514, 113213 (2024)
  • [11] D An, JP Liu, L Lin, Linear combination of Hamiltonian simulation for nonunitary dynamics with optimal state preparation cost, Physical Review Letters, 131(15), 150603 (2023)
  • [12] D An, D Fang, S Jordan, JP Liu, GH Low, J Wang, Efficient quantum algorithm for nonlinear reaction-diffusion equations and energy estimation, Communications in Mathematical Physics, 404, 963-1020 (2023)
  • [13] AM Childs, T Li, JP Liu, C Wang, R Zhang, Quantum algorithms for sampling log-concave distributions and estimating normalizing constants, Advances in Neural Information Processing Systems, 35, 23205-23217 (2022)
  • [14] C Shao, JP Liu, Solving generalized eigenvalue problems by ordinary differential equations on a quantum computer, Proceedings of the Royal Society A, 478(2262), 20210797 (2022)
  • [15] AM Childs, J Leng, T Li, JP Liu, C Zhang, Quantum simulation of real-space dynamics, Quantum, 6, 860 (2022)
  • [16] D An, N Linden, JP Liu, A Montanaro, C Shao, J Wang, Quantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance, Quantum, 5 (2021)
  • [17] AM Childs, JP Liu, A Ostrander, High-precision quantum algorithms for partial differential equations, Quantum, 5, 574 (2021)
  • [18] JP Liu, HØ Kolden, HK Krovi, NF Loureiro, K Trivisa, AM Childs, Efficient quantum algorithm for dissipative nonlinear differential equations, Proceedings of the National Academy of Sciences, 118(35), e2026805118 (2021)
  • [19] C Sun, JP Liu, New stepsizes for the gradient method, Optimization Letters, 14(7), 1943-1955 (2020)
  • [20] AM Childs, JP Liu, Quantum spectral methods for differential equations, Communications in Mathematical Physics, 375, 1427-1457 (2020)

 

更新时间: 2025-10-29 17:00:06