Jin-Peng Liu

jinpeng-liu's picture
Graduate Student (2018-2022)
jliu1219@terpmail.umd.edu
3106 Atlantic Building
Website

Jin-Peng Liu received his Ph.D. in Applied Mathematics & Statistics, and Scientific Computation Program from the University of Maryland in 2022. He received NSF QISE-NET Triplet Award in 2021. He was a long-term visitor at Simons Institute in 2020. He focused mainly on the design and analysis of quantum algorithms for scientific computational problems, including topics such as linear and nonlinear differential equations, quantum dynamics, and stochastic processes, with applications in areas such as fluid dynamics, biology, quantum chemistry, economics, and finance. His adviser was Andrew Childs. After QuICS, he continued on as a Simons Quantum Postdoctoral Fellow at Simons Institute and UC Berkeley, hosted by Umesh Vazirani and Lin Lin.

Publications

2023

D. An, Liu, J. - P., Wang, D., and Zhao, Q., “A theory of quantum differential equation solvers: limitations and fast-forwarding”, 2023.
D. An, Liu, J. - P., and Lin, L., “Linear combination of Hamiltonian simulation for non-unitary dynamics with optimal state preparation cost”, Phys. Rev. Lett., vol. 131, no. 150603, 2023.

2022

D. An, Fang, D., Jordan, S., Liu, J. - P., Low, G. Hao, and Wang, J., “Efficient quantum algorithm for nonlinear reaction-diffusion equations and energy estimation”, 2022.
A. M. Childs, Leng, J., Li, T., Liu, J. - P., and Zhang, C., “Quantum simulation of real-space dynamics”, Quantum, vol. 6, p. 860, 2022.

2021

D. An, Linden, N., Liu, J. - P., Montanaro, A., Shao, C., and Wang, J., “Quantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance”, Quantum 5, 481 (2021), vol. 5, p. 481, 2021.
J. - P. Liu, Kolden, H. Øie, Krovi, H. K., Loureiro, N. F., Trivisa, K., and Childs, A. M., “Efficient quantum algorithm for dissipative nonlinear differential equations”, Proceedings of the National Academy of Sciences, vol. 118, 2021.
A. M. Childs, Liu, J. - P., and Ostrander, A., “High-precision quantum algorithms for partial differential equations”, Quantum 5, 574, vol. 5, no. 574, 2021.

2020

A. M. Childs and Liu, J. - P., “Quantum spectral methods for differential equations”, Commun. Math. Phys. , vol. 375, pp. 1427-1457, 2020.
C. Shao and Liu, J. - P., “Quantum algorithms for the polynomial eigenvalue problems”, 2020.

2019

C. Sun and Liu, J. - P., “New stepsizes for the gradient method”, Optim Lett , 2019.