Research Areas

Quantum Simulation Algorithms, Quantum Scientific Computation, Quantum Machine Learning

Jin-Peng Liu’s research focuses on Quantum for Science and AI+QS. He has developed a series of novel quantum algorithms for

differential equations, sampling, and optimization. He has solved an open problem in 15 years: the first polynomial-time

quantum algorithm for nonlinear differential equations. He has published papers in PNAS, Nat. Commun., PRL, CMP, JCP, Quantum,

and NeurIPS, QIP, TQC. His research has been reported by Quanta, SIAM News, and MATH+. He is serving as an editor of Quantum.

Education

2013-2017 Bachelor, Beihang University- Chinese Academy of Sciences Hua Loo Keng Class

2017-2022 Doctor, University of Maryland, College Park

Work Experience

2024-present Assistant Professor YAU Mathematical Science Center, Tsinghua University

2023-2024 Postdoctoral Associate, Massachusetts Institute of Technology

2022-2023 Simons Quantum Postdoctoral Fellow, University of California, Berkeley

Honors and Awards

2024 ICCM Best Thesis Award (Gold Prize)

2023-2024 NSF Robust Quantum Simulation Seed Grant（Co-PI）

2023 James C. Alexander Prize

2023 MIT CTP Postdoctoral Scholarship

2022 Simons Quantum Postdoctoral Fellowship

2022 Stanford Q-FARM Bloch Fellowship

2021 NSF QISE-NET Triplet Award

Publications

(Google Scholar https://scholar.google.com/citations?user=4dExDoAAAAAJ&hl=en)

[1] Provably Efficient Adiabatic Learning for Quantum-Classical Dynamics (with C.Peng, G-W.Chern, and D.Luo) arXiv:2408.00276

[2] Explicit block encodings of boundary value problems for many-body elliptic operators (with T.Kharazi, A.M.Alkadri, K.K.Mandadapu, and K.B.Whaley) arXiv:2407.18347

[3] Dense outputs from quantum simulations (with L.Lin) Journal of Computational Physics 113213 (2024)

[4] Towards provably efficient quantum algorithms for large-scale machine-learning

models (with J.Liu, M.Liu, Z.Ye, Y.Alexeev, J.Eisert, and L.Jiang) Nature Communications 15, 434 (2024).

[5] Linear combination of Hamiltonian simulation for non-unitary dynamics with optimal state preparation cost (with D.An and L.Lin) Physical Review Letters 131, 150603 (2023)

[6] A theory of quantum differential equation solvers: limitations and fast-forwarding (with D.An, D.Wang, and Q.Zhao) arXiv:2211.05246

[7] Quantum algorithms for sampling log-concave distributions and estimating normalizing constants (with A.M.Childs, T.Li, C.Wang, and R.Zhang) Advances in Neural Information Processing Systems 35, 23205–23217 (NeurIPS 2022)

[8] Efficient quantum algorithm for nonlinear reaction-diffusion equations and energy estimation (with D.An, D.Fang, S.Jordan, G.Low, and J.Wang) Communications in Mathematical Physics 404, 963-1020 (2023)

[9] Quantum simulation of real-space dynamics (A.M.Childs, J.Leng, T.Li,, C.Zhang) Quantum 6, 860 (2022)

[10] Quantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance (with D.An, N.Linden, A.Montanaro, C.Shao, and J.Wang) Quantum 5, 481 (2021)

[11] Efficient quantum algorithm for dissipative nonlinear differential equations (with H. Ø.Holden, H.K.Krovi, N.F.Loureiro, K.Trivisa, and A.M.Childs) Proceedings of the National Academy of Sciences 118, 35 (2021)

[12] Solving generalized eigenvalue problems by ordinary differential equations on a quantum computer (with C.Shao) Proceedings of the Royal Society A 478, 20210797 (2022)

[13] High-precision quantum algorithms for partial differential equations (with A.M.Childs and A.Ostrander) Quantum 5, 574 (2021)

[14] Quantum spectral methods for differential equations (with A.M.Childs) Communications in Mathematical Physics 375, 1427-1457 (2020)

[15] New stepsizes for the gradient method (with C.Sun) Optimization Letters 14, 1943-1955 (2020)