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 by 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, University of California, Berkeley.

Feb 2020: Our paper Quantum spectral methods for differential equations is published in Communications in Mathematical Physics.

Dec 2019: I’m visiting Lawrence Berkeley National Laboratory.

Nov 2019: Our paper New stepsizes for the gradient method is published in Optimization Letters.

Jun 2019: I’m visiting Academy of Mathematics and Systems Science, Chinese Academy of Sciences.

Quantum algorithms for linear and nonlinear differential equations

• Apr 2021: R&D Engineering Group, Goldman Sachs
• Apr 2021: Department of Mathematics, University of Utah

Quantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance

• Mar 2021: Sayas Numerics Seminar, College Park

Efficient quantum algorithm for dissipative nonlinear differential equations

• May 2021: Quantum Lunch Seminar, Los Alamos National Lab
• Apr 2021: Quantum Theory Meeting, Amazon Web Services Center for Quantum Computing
• Apr 2021: IQC-QuICS Math and Computer Science Seminar, College Park
• Mar 2021: SIAM Conference on Computational Science and Engineering (SIAM CSE21), 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

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)

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

Quantum algorithms for the polynomial eigenvalue problems

Changpeng Shao and Jin-Peng Liu

• arXiv:2010.15027

High-precision quantum algorithms for partial differential equations

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

• arXiv:2002.07868

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, 1-13 (2019)
• Presented at ICCOPT 19 and SIAM OP 17 as a contributed talk