My name is Jin-Peng Liu (刘锦鹏). I am a Tenure Track Assistant Professor at YMSC, Tsinghua University.

I was a Postdoctoral Associate at Center for Theoretical Physics, MIT, hosted by Aram Harrow in 2023-2024. I was a Simons Quantum Postdoctoral Fellow at Simons Institute, UC Berkeley, hosted by Umesh Vazirani and Lin Lin in 2022-2023.

I received my Ph.D. in AMSC at University of Maryland in 2022, advised by Andrew Childs. I received my B.S. in Hua Loo Keng Class at Beihang and Chinese Academy of Sciences in 2017, supervised by Ya-xiang Yuan.

My research focuses on *Quantum for Science* and *AI+QS*. I attempt to develop, analyze, and optimize provably efficient quantum algorithms for science and AI problems, including topics: (i) robust quantum simulations; (ii) efficient quantum scientific computation; (iii) scalable quantum machine learning, toward end-to-end applications in areas such as quantum chemistry, biology and epidemiology, fluid dynamics, finance, machine learning, and artificial general intelligence.

Editor: Quantum (JCR Q1, Impact Factor:6.4).

Publications in journals: PNAS, Nat. Commun., PRL, CMP(2), JCP, Quantum(3), Proc. R. Soc. A, and conferences: NeurIPS, QIP(2), TQC.

Media highlights: first-page coverage and annual review in Quanta Magazine, SIAM News, MATH+, Chicago PME News.

Grants/Awards: ICCM Best Thesis Award (Gold Prize), NSF Robust Quantum Simulation Seed Grant (CO-PI), NSF QISE-NET Triplet Award, James C. Alexander Prize.

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

PhD in Applied Mathematics, 2017 - 2022

University of Maryland

BSc in Mathematics, 2017

Beihang University

Sep 2024 - Nov 2024: I teach a YMSC course on Quantum Scientific Computation and Quantum Altificial Intelligence in 2024 Fall.

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.

Jan 2024: I’m thrilled to receive the 2023 ICCM Best Thesis Award (formerly New World Mathematics Award) Doctor Thesis Award, Gold Prize!

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.

**Quantum algorithms for scientific computation and optimization problems**

- Mar 2024: INFORMS Optimization Society Conference (IOS 24), Houston

**Quantum for Science: efficient quantum algorithms for linear and nonlinear dynamics**

- Mar 2024: Quantum Seminar, Rice Quantum Initiative, Houston
- Mar 2024: QI Group Meeting, Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge
- Mar 2024: Quantum Information Science Seminar, Harvard University, Cambridge
- Feb 2024: The MaD Seminar, Center for Data Science and Courant Institute, New York University, New York

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

- Jan 2024: Conference on Quantum Information Processing 2024 (QIP 2024), contributed talk, Taibei

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

- Oct 2023: Workshop I: Quantum Algorithms for Scientific Computation, Program on Mathematical and Computational Challenges in Quantum Computing, Institute for Pure and Applied Mathematics (IPAM QASC 23), University of California, Los Angeles
- Oct 2023: ECE 297 Seminar, Electrical and Computer Engineering Department, University of California, Los Angeles
- Oct 2023: CS 201 Seminar, Computer Science Department, University of California, Los Angeles
- Sep 2023: IEEE International Conference on Quantum Computing and Engineering 2023 (IEEE QCE 23), Bellevue
- Apr 2023: QI Group Meeting, Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge
- Mar 2023: Joint Center for Quantum Information and Computer Science Seminar, University of Maryland
- Mar 2023: Quantum Seminar, Global Technology Applied Research Center, J. P. Morgan Chase & Co, New York
- Mar 2023: Applied Mathematics and Computational Science Colloquium, AMCS Graduate Group, University of Pennsylvania, Philadelphia
- Mar 2023: Computational and Applied Mathematics Colloquium, Department of Mathematics, Pennsylvania State University, University Park
- Feb 2023: Applied Mathematics Seminar, Department of Mathematics, Stanford University
- Feb 2023: Applied Mathematics Seminar, Department of Mathematics, University of California, Berkeley

**Efficient quantum algorithms for regularized optimization**

- May 2023: SIAM Conference on Optimization 2023 (SIAM OP 23), Seattle

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

- Sep 2023: IEEE International Conference on Quantum Computing and Engineering 2023 (IEEE QCE 23), Bellevue
- 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

**Provably Efficient Adiabatic Learning for Quantum-Classical Dynamics**

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

**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

**Dense outputs from quantum simulations**

Jin-Peng Liu and Lin Lin

**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

**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

**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

**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

**Instructor** at YMSC, Tsinghua University:

- Fall 2024: Quantum Scientific Computation and Quantum Altificial Intelligence, YMSC course.

**Editor**:
Quantum.

**Reviewer**:
ACM TQC,
AQT,
CMP,
CCP,
CMAME,
ICALP,
ICML,
IEEE TQE,
JCP,
Journal of Physics A,
JSC,
M2AN,
Nature Communications,
NJP,
npj QI,
Numerical Algorithms,
PRE,
PRL,
PR Research,
PRX Quantum,
Physics of Fluids,
Physics of Plasmas,
Quantitative Finance,
Quantum,
QIC,
QIP,
Science Bulletin,
SINUM,
SPIN,
TQC.

**Session Chair**:
IOS 24,
SIAM OP 23,
IPAM QNLA 22.

**CO-Principal Investigator**: NSF Robust Quantum Simulation Seed Grant: End-to-end applications of quantum linear system and differential equation algorithms.

- Jan 2024: ICCM Best Thesis Award (Gold Prize)
- Sep 2023 - Oct 2023: Long-term visitor at Institute for Pure and Applied Mathematics, UCLA

- May 2023: Editor of Quantum
- May 2023: James C. Alexander Prize for Graduate Research in Mathematics
- Apr 2023 - May 2023: Long-term visitor at Center for Theoretical Physics, MIT
- Mar 2023: NSF Robust Quantum Simulation Seed Grant (CO-PI)

- 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
- Feb 2020 - Mar 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