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

• arXiv:2303.03428

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

Dong An, Jin-Peng Liu, and Lin Lin

• arXiv:2303.01029

A theory of quantum differential equation solvers: limitations and fast-forwarding

Dong An, Jin-Peng Liu, Daochen Wang, and Qi Zhao

• arXiv:2211.05246

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

• arXiv:2203.17006
• Quantum 6, 860 (2022)

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

• arXiv:2010.15027
• Proceedings of the Royal Society A 478, 20210797 (2022)

High-precision quantum algorithms for partial differential equations

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

• arXiv:2002.07868
• Quantum 5, 574 (2021)

Quantum spectral methods for differential equations

Andrew M. Childs and Jin-Peng Liu

• arXiv:1901.00961
• Communications in Mathematical Physics 375, 1427-1457 (2020)

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