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Jin-Peng Liu

Simons Postdoctoral Fellow

UC Berkeley

Biography

My name is Jin-Peng Liu (刘锦鹏). I am a Simons Quantum Postdoctoral Fellow at Simons Institute and UC Berkeley, hosted by Umesh Vazirani and Lin Lin. In Fall of 2023, I will be a postdoctoral associate at the Center for Theoretical Physics, Massachusetts Institute of Technology, mainly hosted by Aram Harrow.

I received my Ph.D. degree in AMSC program at University of Maryland in 2022, advised by Andrew Childs. I received NSF QISE-NET Triplet Award in 2021. 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.

My research focuses on Quantum for Science. I attempt to develop, analyze, and optimize provably efficient quantum algorithms for computational challenges in natural and data sciences, including quantum simulations, quantum ODE/PDE solvers, q-sampling, and quantum gradient descent, toward end-to-end applications in areas such as quantum chemistry, biology and epidemiology, fluid dynamics, finance, statistics, optimization, and machine learning.

Publications in journals: PNAS, CMP, Proc. R. Soc. A, Quantum, and conferences: NeurIPS, QIP, TQC.

Media highlights: first-page coverage and annual review in Quanta Magazine.

Interests

  • Quantum Computing
  • Quantum Information
  • Quantum Algorithms
  • Quantum Machine Learning

Education

  • PhD in Applied Mathematics, 2017 - 2022

    University of Maryland

  • BSc in Mathematics, 2017

    Beihang University

Posts

Nov 2022: Our paper Quantum algorithms for sampling log-concave distributions and estimating normalizing constants is accepted by NeurIPS 2022 and QIP 2023.

Jul 2022 - Aug 2022: I’m a long-term visitor of The Center for Theoretical Physics, Massachusetts Institute of Technology.

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!

Jan 2022: I’m a session chair and an invited speaker at Workshop on Quantum Numerical Linear Algebra, Institute for Pure and Applied Mathematics, UCLA.

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.

Nov 2021: I’m an invited speaker at The 74th Annual Meeting of the Division of Fluid Dynamics.

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, Berkeley.

Feb 2020: Our paper Quantum spectral methods for differential equations is published in Communications in Mathematical Physics.

Talks

Towards provably efficient quantum algorithms for nonlinear dynamics and large-scale machine learning models

  • (Upcoming) Mar 2023: Applied Mathematics and Computational Science Colloquium, AMCS Graduate Group, University of Pennsylvania
  • (Upcoming) Mar 2023: Computational and Applied Mathematics Colloquium, Department of Mathematics, Pennsylvania State University
  • (Upcoming) Feb 2023: Applied Mathematics Seminar, Department of Mathematics, Stanford University
  • (Upcoming) Feb 2023: Applied Mathematics Seminar, Department of Mathematics, University of California, Berkeley

Quantum algorithms for sampling log-concave distributions and estimating normalizing constants

  • (Upcoming) Feb 2023: Conference on Quantum Information Processing 2023 (QIP 2023), contributed talk, Ghent, Belgium
  • Dec 2022: Conference on Neural Information Processing Systems 2022 (NeurIPS 22), New Orleans
  • Nov 2022: Theory of Computation Group Seminar, Department of Computer Science, Columbia University, New York
  • Sep 2022: Quantum Gathering, NSF Challenge Institute for Quantum Computation, University of California, Berkeley

Efficient quantum algorithms for nonlinear ODEs and PDEs

  • Dec 2022: Quantum Seminar, Pritzker School of Molecular Engineering, University of Chicago
  • Nov 2022: Applied Mathematics Colloquium, Department of Applied Physics and Applied Mathematics, Columbia University, New York
  • Aug 2022: Quantum Information Seminar, Rhodes Information Initiative, Duke University, Durham
  • Jul 2022: QI Group Meeting, Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge
  • Jan 2022: Workshop on Quantum Numerical Linear Algebra, Institute for Pure and Applied Mathematics, University of California, Los Angeles

Quantum algorithms for linear and nonlinear differential equations

  • Dec 2021: Perimeter Institute Quantum Discussions, Waterloo
  • Apr 2021: R&D Engineering Group, Goldman Sachs, New York
  • Apr 2021: Department of Mathematics, University of Utah, Salt Lake City

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), contributed talk, Riga, Latvia
  • Mar 2021: Sayas Numerics Seminar, College Park

Efficient quantum algorithm for dissipative nonlinear differential equations

  • Dec 2021: QuantumFest 2021, Harvard Quantum Initiative, Cambridge
  • Nov 2021: Annual Meeting of the Division of Fluid Dynamics 2021 (APS DFD 21), Phoenix
  • May 2021: Quantum Lunch Seminar, Los Alamos National Lab, New Mexico
  • Apr 2021: Quantum Theory Meeting, Amazon Web Services Center for Quantum Computing, Pasadena
  • 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

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

Dong An, Di Fang, Stephen Jordan, Jin-Peng Liu, Guang Hao Low, and Jiasu Wang

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

Experience

 
 
 
 
 

Simons Quantum Postdoctoral Fellow

Simons Institute for the Theory of Computing and University of California, Berkeley

Aug 2022 – Aug 2023 Berkeley, CA

  • Jul 2022 - Aug 2022: Long-term visitor at Center for Theoretical Physics, MIT

  • May 2022 - Jun 2022: Long-term visitor at Simons Institute, UC Berkeley

 
 
 
 
 

Doctoral Student

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

Sep 2017 – May 2022 College Park, MD
  • Jan 2022: Graduate School’s Outstanding Research Assistant Award
  • 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 (rank: 1/3987)
  • 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

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