Jin-Peng Liu

Doctoral Student of Applied Mathematics

University of Maryland

Biography

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 ODE and PDE solvers, quantum optimization, and quantum computational finance. I am also interested in classical algorithms for nonlinear optimization problems.

Interests

Quantum Computing
Numerical Analysis
Nonlinear Optimization

Education

PhD in Applied Mathematics, 2017 - 2022
University of Maryland
BSc in Mathematics, 2017
Beihang University

Posts

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.

Talks

High-precision quantum algorithms for ODEs and PDEs

Jun 2020: Center for Quantum Computing, Peng Cheng Laboratory, Shenzhen
Apr 2020: Microsoft Quantum Redmond Group, Microsoft Research, Redmond

High-precision quantum algorithms for partial differential equations

Mar 2020: Department of Mathematics, University of California, Berkeley

Quantum computation for linear algebra (QCLA)

Mar 2020: Department of Mathematics, University of California, Los Angeles

Quantum algorithms for differential equations and optimization

Jun 2019: Academy of Mathematics and Systems Science, Chinese Academy of Sciences

Publications

High-precision quantum algorithms for partial differential equations

Andrew M. Childs, Jin-Peng Liu, and Aaron Ostrander

arXiv:2002.07868

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

Optimization Letters, 1-13 (2019)
Presented at ICCOPT 19 and SIAM OP 17 as a contributed talk

Service

Reviewer of ACM Transactions on Quantum Computing, International Conference on Machine Learning, Numerical Algorithms, Physical Review Letters, PRX Quantum, Quantum, Science Bulletin.

Experience

Doctoral Student

Applied Mathematics & Statistics, and Scientific Computation, University of Maryland

Sep 2017 – Present College Park, MD

Feb 2020: Visiting Student in Simons Institute, University of California, Berkeley
Sep 2017: Dean’s Fellowship

Undergraduate Student

Chinese Academy of Sciences Hua Loo Keng Class in Mathematics, Beihang University

Sep 2013 – Jun 2017 Beijing

Jun 2017: Representative of Beihang Graduation Ceremony
Nov 2016: Shenyuan Golden Medal (rank: 10/3987)
Dec 2015: Outstanding Student of the Year (rank: 10/3987)
Nov 2015: Mathematics Star Award (rank: 1/115)
May 2015: President of Beijing Association for Collegiate Reading