Jul 2025 - Aug 2025: I teach a Yau Mathcamp course on Quantum Linear Algebra and Quantum Artificial Intelligence at SIMIS in summer of 2025.

Jun 2025: NSF’s CIQC media report: CIQC’s Impact in Action: Building Quantum Careers in Mathematics.

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.

Arbitrary Boundary Conditions and Constraints in Quantum Algorithms for Differential Equations via Penalty Projections

Philipp Schleich, Tyler Kharazi, Xiangyu Li, Jin-Peng Liu, Alan Aspuru-Guzik, and Nathan Wiebe

• arXiv:2506.21751

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
• Quantum 9, 1764 (2025)

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
• Communications in Mathematical Physics 406, 189 (2025)

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