Jan 2025: I’m thrilled to receive the 2023 ICCM Graduate Thesis Award, Gold Prize (formerly New World Mathematics Award)! Media report in Shanghai News.

Sep 2024 - Nov 2024: I teach a YMSC course on Quantum Scientific Computation and Quantum Altificial Intelligence. I also organize a quantum AI seminar and co-organize a quantum information seminar in Fall of 2024.

Aug 2024: I’m thrilled to join YMSC, Tsinghua University as a Tenure Track Assistant Professor! Postdoc, PhD, and RA student positions are available. Please reach out to me via email.

Jun 2024: Our paper Dense outputs from quantum simulations is accepted by Journal of Computational Physics.

Apr 2024: Our paper Linear combination of Hamiltonian simulation for non-unitary dynamics with optimal state preparation cost is accepted by Physical Review Letters and QIP 2024 and is highlighted by SIAM News.

Feb 2024: Our paper Towards provably efficient quantum algorithms for large-scale machine-learning models is accepted by Nature Communications and is highlighted by MATH+ and Chicago PME News.

Sep 2023 - Oct 2023: I’m a long-term core participant and an invited speaker at Program on Mathematical and Computational Challenges in Quantum Computing, Institute for Pure and Applied Mathematics, UCLA.

Sep 2023: Our paper Efficient quantum algorithm for nonlinear reaction-diffusion equations and energy estimation is accepted by Communications in Mathematical Physics.

Sep 2023: I’m invited to present two talks about quantum algorithms for differential equations and financial applications at IEEE QCE 23.

May 2023: I receive the James C. Alexander Prize for Graduate Research in Mathematics.

May 2023: I serve as an editor of Quantum.

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.

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 at Simons Institute, Berkeley and defer the CTP Postdoctoral Associate at Center for Theoretical Physics, Massachusetts Institute of Technology!

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.

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.

Feb 2020 - Mar 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.

Infinite-dimensional Extension of the Linear Combination of Hamiltonian Simulation: Theorems and Applications

Rundi Lu, Hao-En Li, Zhengwe Liu, and Jin-Peng Liu

• arXiv:2502.19688

Towards efficient quantum algorithms for diffusion probability models

Yunfei Wang, Ruoxi Jiang, Yingda Fan, Xiaowei Jia, Jens Eisert, Junyu Liu, and Jin-Peng Liu

• arXiv:2502.14252

Toward end-to-end quantum simulation for protein dynamics

Zhenning Liu, Xiantao Li, Chunhao Wang, and Jin-Peng Liu

• arXiv:2411.03972

Provably Efficient Adiabatic Learning for Quantum-Classical Dynamics

Changnan Peng, Jin-Peng Liu, Gia-Wei Chern, and Di Luo

• arXiv:2408.00276

Explicit block encodings of boundary value problems for many-body elliptic operators

Tyler Kharazi, Ahmad M. Alkadri, Jin-Peng Liu, Kranthi K. Mandadapu, and K. Birgitta Whaley

• arXiv:2407.18347

Dense outputs from quantum simulations

Jin-Peng Liu and Lin Lin

• arXiv:2307.14441
• Journal of Computational Physics, 113213 (2024)

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

Junyu Liu, Minzhao Liu, Jin-Peng Liu, Ziyu Ye, Yunfei Wang, Yuri Alexeev, Jens Eisert, and Liang Jiang

• arXiv:2303.03428
• Nature Communications 15, 434 (2024)
• Presented at IEEE QCE 2023 and IPAM QASC 2023 as an invited talk
• Media highlight in MATH+ and Chicago PME News

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
• Physical Review Letters 131, 150603 (2023)
• Presented at Qsim 2023 and IPAM QASC 2023 as an invited talk
• Presented at QIP 2024 as a contributed talk
• Media highlight in SIAM News

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
• Advances in Neural Information Processing Systems 35, 23205–23217 (NeurIPS 2022)
• Presented at QIP 2023 as a contributed talk
• Presented at IEEE QCE 2023 as an invited talk

Efficient quantum algorithm for nonlinear reaction-diffusion equations and energy estimation

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

• arXiv:2205.01141
• Communications in Mathematical Physics 404, 963-1020 (2023)
• 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
• 2021 Annual Review in Quanta Magazine: The Year in Math and Computer Science
• 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