Host: Jin-Peng Liu 刘锦鹏

Abstract: Simulation of continuous-time evolution requires time discretization on both classical and quantum computers. A finer time step improves simulation precision but it inevitably leads to increased computational efforts. This is particularly costly for today’s noisy intermediate-scale quantum computers, where notable gate imperfections limit the circuit depth that can be executed at a given accuracy. Classical adaptive solvers are well developed to save numerical computation times. However, it remains an outstanding challenge to make optimal usage of the available quantum resources by means of adaptive time steps. Here, we introduce a quantum algorithm to solve this problem, providing a controlled solution of the quantum many-body dynamics of local observables. The key conceptual element of our algorithm is a feedback loop that self-corrects the simulation errors by adapting time steps, thereby significantly outperforming conventional Trotter schemes on a fundamental level and reducing the circuit depth. It even allows for a controlled asymptotic long-time error, where the usual Trotterized dynamics faces difficulties. Another key advantage of our quantum algorithm is that any desired conservation law can be included in the self-correcting feedback loop, which has a potentially wide range of applicability. I will also discuss how to use Reinforcement Learning to further optimize the adaptive time steps.

Bio: 赵宏政，北京大学物理学院凝聚态所助理教授。2017年于山东大学泰山学堂本科毕业，2021年伦敦帝国理工博士毕业，随后两年在德国马克斯-普朗克复杂系统物理研究所从事博士后研究工作，后于2023年加入北大物理学院。长期从事非平衡多体动力学理论研究，研究兴趣包括量子热化，驱动系统，强关联系统，量子模拟与量子计算。