My name is Jin-Peng Liu (刘锦鹏). I am a Tenure Track Assistant Professor at YMSC, Tsinghua University.
I was a Postdoctoral Associate at Center for Theoretical Physics, MIT, hosted by Aram Harrow in 2023-2024. I was a Simons Quantum Postdoctoral Fellow at Simons Institute, UC Berkeley, hosted by Umesh Vazirani and Lin Lin in 2022-2023.
I received my Ph.D. in AMSC at University of Maryland in 2022, advised by Andrew Childs. I received my B.S. in Hua Loo Keng Class at Beihang and Chinese Academy of Sciences in 2017, supervised by Ya-xiang Yuan.
My research focuses on Quantum for Science and AI+QS. I attempt to develop, analyze, and optimize provably efficient quantum algorithms for science and AI problems, including topics: (i) robust quantum simulations; (ii) efficient quantum scientific computation; (iii) scalable quantum machine learning, toward end-to-end applications in areas such as quantum chemistry, biology and epidemiology, fluid dynamics, finance, machine learning, and artificial general intelligence.
Editor: Quantum (JCR Q1, Impact Factor:6.4).
Publications in journals: PNAS, Nat. Commun., PRL, CMP(2), JCP, Quantum(3), Proc. R. Soc. A, and conferences: NeurIPS, QIP(2), TQC.
Media highlights: first-page coverage and annual review in Quanta Magazine, SIAM News, MATH+, Chicago PME News.
Grants/Awards: ICCM Best Thesis Award (Gold Prize), NSF Robust Quantum Simulation Seed Grant (CO-PI), NSF QISE-NET Triplet Award, James C. Alexander Prize.
PhD in Applied Mathematics, 2017 - 2022
University of Maryland
BSc in Mathematics, 2017
Beihang University
Sep 2024 - Nov 2024: I teach a YMSC course on Quantum Scientific Computation and Quantum Altificial Intelligence in 2024 Fall.
Aug 2024: I’m thrilled to join YMSC, Tsinghua University as a Tenure Track Assistant Professor! Postdoc, PhD, and RA student positions are available. Please reach out to me via email.
Jun 2024: Our paper Dense outputs from quantum simulations is accepted by Journal of Computational Physics.
Apr 2024: Our paper Linear combination of Hamiltonian simulation for non-unitary dynamics with optimal state preparation cost is accepted by Physical Review Letters and QIP 2024 and is highlighted by SIAM News.
Feb 2024: Our paper Towards provably efficient quantum algorithms for large-scale machine-learning models is accepted by Nature Communications and is highlighted by MATH+ and Chicago PME News.
Jan 2024: I’m thrilled to receive the 2023 ICCM Best Thesis Award (formerly New World Mathematics Award) Doctor Thesis Award, Gold Prize!
Sep 2023 - Oct 2023: I’m a long-term core participant and an invited speaker at Program on Mathematical and Computational Challenges in Quantum Computing, Institute for Pure and Applied Mathematics, UCLA.
Sep 2023: Our paper Efficient quantum algorithm for nonlinear reaction-diffusion equations and energy estimation is accepted by Communications in Mathematical Physics.
Sep 2023: I’m invited to present two talks about quantum algorithms for differential equations and financial applications at IEEE QCE 23.
May 2023: I receive the James C. Alexander Prize for Graduate Research in Mathematics.
May 2023: I serve as an editor of Quantum.
Mar 2023: I become a CO-PI of NSF Robust Quantum Simulation Seed Grant: End-to-end applications of quantum linear system and differential equation algorithms.
Nov 2022: Our paper Quantum algorithms for sampling log-concave distributions and estimating normalizing constants is accepted by NeurIPS 2022 and QIP 2023.
May 2022 - Jun 2022: I’m a long-term visitor of Extended Reunion: The Quantum Wave in Computing Program, Simons Institute, Berkeley.
May 2022: I obtained my Ph.D. degree!
Apr 2022: I successfully defended my Ph.D. dissertation!
Mar 2022: As a QISE-NET Triplet awardee, I’m invited to present at QISE-NET Reception, APS March Meeting in Chicago.
Feb 2022: I’m thrilled to accept the Simons Quantum Postdoctoral Fellowship at Simons Institute, Berkeley and defer the CTP Postdoctoral Associate at Center for Theoretical Physics, Massachusetts Institute of Technology!
Jan 2022: I receive the Graduate School’s Outstanding Research Assistant Award.
Dec 2021: I’m invited to visit Harvard Quantum Initiative and give a talk at HQI QuantumFest 2021.
Aug 2021: Our paper Efficient quantum algorithm for dissipative nonlinear differential equations is published in Proceedings of the National Academy of Sciences (PNAS).
Jun 2021: I’m an applied scientist intern at Amazon Web Services Center for Quantum Computing this summer.
Jun 2021: Our paper Quantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance is accepted by TQC 2021 and published in Quantum.
Feb 2021: I am selected as a NSF Quantum Information Science and Engineering Network (QISE-NET) Triplet Awardee. I benefit from the mentorship of QuICS, University of Maryland and Microsoft Research Quantum.
Jan 2021: Our paper Efficient quantum algorithm for dissipative nonlinear differential equations is highlighted by a front-page coverage in Quanta Magazine: New Quantum Algorithms Finally Crack Nonlinear Equations.
Feb 2020 - Mar 2020: I’m a long-term visitor of The Quantum Wave in Computing Program, Simons Institute, Berkeley.
Feb 2020: Our paper Quantum spectral methods for differential equations is published in Communications in Mathematical Physics.
Quantum algorithms for scientific computation and optimization problems
Quantum for Science: efficient quantum algorithms for linear and nonlinear dynamics
Linear combination of Hamiltonian simulation for non-unitary dynamics with optimal state preparation cost
Towards provably efficient quantum algorithms for nonlinear dynamics and large-scale machine learning models
Efficient quantum algorithms for regularized optimization
Quantum algorithms for sampling log-concave distributions and estimating normalizing constants
Efficient quantum algorithms for nonlinear ODEs and PDEs
Quantum algorithms for linear and nonlinear differential equations
Quantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance
Efficient quantum algorithm for dissipative nonlinear differential equations
High-precision quantum algorithms for ODEs and PDEs
High-precision quantum algorithms for partial differential equations
Quantum computation for linear algebra (QCLA)
Quantum algorithms for differential equations and optimization
Toward end-to-end quantum simulation for protein dynamics
Zhenning Liu, Xiantao Li, Chunhao Wang, and Jin-Peng Liu
Provably Efficient Adiabatic Learning for Quantum-Classical Dynamics
Changnan Peng, Jin-Peng Liu, Gia-Wei Chern, and Di Luo
Explicit block encodings of boundary value problems for many-body elliptic operators
Tyler Kharazi, Ahmad M. Alkadri, Jin-Peng Liu, Kranthi K. Mandadapu, and K. Birgitta Whaley
Dense outputs from quantum simulations
Jin-Peng Liu and Lin Lin
Towards provably efficient quantum algorithms for large-scale machine learning models
Junyu Liu, Minzhao Liu, Jin-Peng Liu, Ziyu Ye, Yunfei Wang, Yuri Alexeev, Jens Eisert, and Liang Jiang
Linear combination of Hamiltonian simulation for non-unitary dynamics with optimal state preparation cost
Dong An, Jin-Peng Liu, and Lin Lin
A theory of quantum differential equation solvers: limitations and fast-forwarding
Dong An, Jin-Peng Liu, Daochen Wang, and Qi Zhao
Quantum algorithms for sampling log-concave distributions and estimating normalizing constants
Andrew M. Childs, Tongyang Li, Jin-Peng Liu, Chunhao Wang, and Ruizhe Zhang
Efficient quantum algorithm for nonlinear reaction-diffusion equations and energy estimation
Jin-Peng Liu, Dong An, Di Fang, Jiasu Wang, Guang Hao Low, and Stephen Jordan
Quantum simulation of real-space dynamics
Andrew M. Childs, Jiaqi Leng, Tongyang Li, Jin-Peng Liu, and Chenyi Zhang
Quantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance
Dong An, Noah Linden, Jin-Peng Liu, Ashley Montanaro, Changpeng Shao, and Jiasu Wang
Efficient quantum algorithm for dissipative nonlinear differential equations
Jin-Peng Liu, Herman Øie Kolden, Hari K. Krovi, Nuno F. Loureiro, Konstantina Trivisa, and Andrew M. Childs
Solving generalized eigenvalue problems by ordinary differential equations on a quantum computer
Changpeng Shao and Jin-Peng Liu
High-precision quantum algorithms for partial differential equations
Andrew M. Childs, Jin-Peng Liu, and Aaron Ostrander
Quantum spectral methods for differential equations
Andrew M. Childs and Jin-Peng Liu
New stepsizes for the gradient method
Cong Sun and Jin-Peng Liu
Instructor at YMSC, Tsinghua University:
Editor: Quantum.
Reviewer: ACM TQC, AQT, CMP, CCP, CMAME, ICALP, ICML, IEEE TQE, JCP, Journal of Physics A, JSC, M2AN, Nature Communications, NJP, npj QI, Numerical Algorithms, PRE, PRL, PR Research, PRX Quantum, Physics of Fluids, Physics of Plasmas, Quantitative Finance, Quantum, QIC, QIP, Science Bulletin, SINUM, SPIN, TQC.
Session Chair: IOS 24, SIAM OP 23, IPAM QNLA 22.
CO-Principal Investigator: NSF Robust Quantum Simulation Seed Grant: End-to-end applications of quantum linear system and differential equation algorithms.