My name is Jin-Peng Liu (刘锦鹏). I am a fourth-year doctoral student of AMSC program at the University of Maryland, advised by Andrew Childs.
Prior to that, I received my B.S. degree in Chinese Academy of Sciences Hua Loo Keng Class at the Beihang University in 2017, supervised by Ya-xiang Yuan.
I aim to develop quantum algorithms with sup-polynomial speed-ups over classical algorithms, including topics such as quantum solvers for linear and nonlinear differential equations, quantum simulation, quantum optimization and machine learning, and quantum computational finance. I am also interested in classical algorithms for nonlinear optimization.
PhD in Applied Mathematics, 2017 - 2022
University of Maryland
BSc in Mathematics, 2017
Beihang University
Jan 2021: Our paper Efficient quantum algorithm for dissipative nonlinear differential equations is introduced by Coverage in Quanta Magazine: New Quantum Algorithms Finally Crack Nonlinear Equations.
Feb 2020 - Mar 2020: I’m a visiting student of The Quantum Wave in Computing Program, Simons Institute, University of California, Berkeley.
Feb 2020: Our paper Quantum spectral methods for differential equations is published in Communications in Mathematical Physics.
Dec 2019: I’m visiting Lawrence Berkeley National Laboratory.
Nov 2019: Our paper New stepsizes for the gradient method is published in Optimization Letters.
Jun 2019: I’m visiting Academy of Mathematics and Systems Science, Chinese Academy of Sciences.
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
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
Quantum algorithms for the polynomial eigenvalue problems
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