Abstract:

Quantum computers are expected to simulate unitary dynamics (i.e., Hamiltonian simulation) much faster than classical computers. However, most applications in scientific computing involve non-unitary dynamics and processes. In this talk, we will discuss a recently proposed quantum algorithm for solving general linear differential equations. The idea of the algorithm is to reduce general differential equations to a linear combination of Hamiltonian simulation (LCHS) problems. For the first time, this approach allows quantum algorithms to solve linear differential equations with near-optimal dependence on all parameters. Additionally, we will discuss a hybrid quantum-classical differential equation algorithm based on LCHS, which may be more feasible on near-term quantum devices.

(This talk assumes no prior knowledge in quantum computation and information.)

Bio:

Dong An is a QuICS Hartree Postdoctoral Fellow in quantum information science at the University of Maryland. He received his PhD in applied mathematics from the University of California, Berkeley in 2021, advised by Professor Lin Lin, and his BS degree in computational mathematics from Peking University in 2016. His research interests lie at the intersection of quantum computing and applied mathematics, with a focus on quantum algorithms for scientific computing problems, such as large-scale linear systems of equations, Hamiltonian simulation, and differential equations.

Tencent meeting: 115-577-496

https://meeting.tencent.com/dm/3WtTXRLrlusO