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. 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.

Grants/Awards: NSF Robust Quantum Simulation Seed Grant (CO-PI), NSF QISE-NET Triplet Award.

- Quantum Computing
- Quantum Information
- Quantum Algorithms
- Quantum Machine Learning

PhD in Applied Mathematics, 2017 - 2022

University of Maryland

BSc in Mathematics, 2017

Beihang University

Mar 2023: I become a CO-PI of NSF Robust Quantum Simulation Seed Grant: End-to-end applications of quantum linear system and differential equation algorithms.

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.

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

*(Upcoming)*Mar 2023: Joint Center for Quantum Information and Computer Science Seminar, University of Maryland*(Upcoming)*Mar 2023: Quantum Seminar, Global Technology Applied Research Center, J. P. Morgan Chase & Co.- Mar 2023: Applied Mathematics and Computational Science Colloquium, AMCS Graduate Group, University of Pennsylvania
- Mar 2023: Computational and Applied Mathematics Colloquium, Department of Mathematics, Pennsylvania State University
- Feb 2023: Applied Mathematics Seminar, Department of Mathematics, Stanford University
- Feb 2023: Applied Mathematics Seminar, Department of Mathematics, University of California, Berkeley

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

- 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

**Towards provably efficient quantum algorithms for large-scale machine learning models**

Junyu Liu, Mingzhao Liu, Jin-Peng Liu, Ziyu Ye, Yuri Alexee, Jens Eisert, and Liang Jiang

**Linear combination of Hamiltonian simulation for non-unitary dynamics with optimal state preparation cost**

Dong An, Jin-Peng Liu, and Lin Lin

**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

- arXiv:2210.06539
- Presented at NeurIPS 2022 as an accepted paper
- To appear in QIP 2023 as a contributed talk

**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

- arXiv:2205.01141
- Presented at IPAM QNLA 2022 as an invited talk

**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

- arXiv:2012.06283
- Quantum 5, 481 (2021)
- Presented at TQC 2021 as a contributed talk

**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

- arXiv:2011.03185
- Front-page Coverage in Quanta Magazine: New Quantum Algorithms Finally Crack Nonlinear Equations
- Proceedings of the National Academy of Sciences 118, 35 (2021)
- Presented at SIAM CSE 2021 and APS DFD 2021 as an invited talk

**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

- Optimization Letters 14, 1943-1955 (2020)
- Presented at ICCOPT 19 and SIAM OP 17 as a contributed talk

**CO-Principal Investigator**of NSF Robust Quantum Simulation Seed Grant: End-to-end applications of quantum linear system and differential equation algorithms.**Reviewer**of ACM Transactions on Quantum Computing, Advanced Quantum Technologies, Communications in Computational Physics, Computer Methods in Applied Mechanics and Engineering, Conference on Quantum Information Processing, Conference on the Theory of Quantum Computation, Communication and Cryptography, International Colloquium on Automata Languages and Programming, International Conference on Machine Learning, Journal of Computational Physics, Journal of Physics A, Nature Communications, New Journal of Physics, npj Quantum Information, Numerical Algorithms, Physical Review E, Physical Review Letters, Physical Review Research, Physical Review X Quantum, Physics of Fluids, Physics of Plasmas, Quantitative Finance, Quantum, Quantum Information and Computation, Science Bulletin, SPIN.

Mar 2023: NSF Robust Quantum Simulation Seed Grant (CO-PI)

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

- 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

- 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