Host: Jin-Peng Liu 刘锦鹏

Abstract: Adiabatic quantum computing (AQC) is a powerful framework for state preparation, yet its practical utility is often hindered by sub-optimal computational complexity in both continuous evolution and its digital circuit implementation. In this talk, we present two recent works that address these limits. First, we introduce a nonlinear adaptive scheduling function that improves the gap dependence of continuous AQC from quadratic to linear, yielding optimal evolution times. Second, we demonstrate that when simulating AQC on digital quantum devices, the discrete time-step sizes can be much larger than conventionally expected. We also show that first-order Trotterization can even achieve exponential convergence under boundary cancellation conditions.

Bio: Dong An is an Assistant Professor at Beijing International Center for Mathematical Research (BICMR), Peking University.

He worked as a Hartree Postdoctoral Fellow at Joint Center for Quantum Information and Computer Science (QuICS), University of Maryland, in 2021-2024. He received his Ph.D. in Applied Mathematics from University of California, Berkeley in 2021, advised by Professor Lin Lin, and his B.S. degree in computational mathematics from Peking University in 2016.

Dong's research interest is Quantum X Applied Mathematics, which is primarily twofold: Quantum algorithms for classical scientific computing problems, and classical algorithms for quantum many-body problems.