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Jin-Peng Liu

Doctoral Student of Applied Mathematics

University of Maryland

Biography

My name is Jin-Peng Liu (刘锦鹏). I am a fifth-year Ph.D. candidate of AMSC program at University of Maryland, advised by Andrew Childs. I received NSF QISE-NET Triplet Award in 2021. I was a long-term visitor at Simons Institute in 2020.

Prior to that, I received my B.S. degree in Chinese Academy of Sciences Hua Loo Keng Class at Beihang University in 2017, supervised by Ya-xiang Yuan.

I focus 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. I attempt to build a bridge between classical and quantum mechanics, understand and optimize the power of quantum computation, and discover new quantum speedups over classical algorithms with provable guarantees.

Interests

  • Quantum Computing
  • Quantum Algorithms
  • Numerical Analysis

Education

  • PhD in Applied Mathematics, 2017 - 2022

    University of Maryland

  • BSc in Mathematics, 2017

    Beihang University

Posts

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.

Jan 2020 - May 2020: I’m a long-term visitor 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

Quantum algorithms for linear and nonlinear differential equations

  • Apr 2021: R&D Engineering Group, Goldman Sachs
  • Apr 2021: Department of Mathematics, University of Utah

Quantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance

  • Jul 2021: Conference on the Theory of Quantum Computation, Communication and Cryptography 2021 (TQC 21), Riga, Latvia
  • Mar 2021: Sayas Numerics Seminar, College Park

Efficient quantum algorithm for dissipative nonlinear differential equations

  • May 2021: Quantum Lunch Seminar, Los Alamos National Lab
  • Apr 2021: Quantum Theory Meeting, Amazon Web Services Center for Quantum Computing
  • Apr 2021: IQC-QuICS Math and Computer Science Seminar, College Park
  • Mar 2021: SIAM Conference on Computational Science and Engineering 2021 (SIAM CSE 21), Fort Worth
  • Dec 2020: AMSS-UTS Joint Workshop on Quantum Computing, Chinese Academy of Sciences, Beijing

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

  • Dec 2020: Quantum Winter Lecture, BosonQ Psi
  • 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: Institute of Computational Mathematics and Scientific/Engineering Computing, Chinese Academy of Sciences, Beijing

Publications

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

Experience

 
 
 
 
 

Doctoral Student

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

Sep 2017 – Present College Park, MD
  • Jun 2021 - Aug 2021: applied scientist intern at Amazon Web Services
  • Feb 2021: NSF QISE-NET Triplet Award
  • Jan 2020 - May 2020: Long-term visitor at Simons Institute
  • Sep 2017: Dean’s Fellowship of AMSC program
 
 
 
 
 

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

Contact

  • jliu1219@terpmail.umd.edu
  • Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD 20742