报告题目：Optimization and Representability of time-dependent Neural Quantum States: a study of the 1D critical quantum Ising model

报 告 人：Markus Michael Schmitt

内容摘要：In recent years, neural quantum states have emerged as a competitive and powerful numerical approach for many body systems. While they provide a flexible and scalable ansatz, able to represent any state as suggested by the function-approximation theorem, their practical limitations are still opaque, in particular regarding representability and optimization. In this work we investigate these questions within the framework of variational Monte Carlo on the example of the time evolution of the critical transverse-field Ising model in one dimension. Even for moderate system sizes, the departure from the exact solution occurs very early in the dynamics, allowing us to systematically analyze its origin. Our findings suggest that in this case the key challenge is one that has received little attention so far: a dynamical instability of the non-linear equations of motion.

报告人简介：

2018: PhD in Physics, Göttingen University (Germany)

2018: Postdoc @ MPI PKS, Dresden (Germany)

2018-2020: Postdoc @ UC Berkeley

2020-2022: Postdoc @ University of Cologne (Germany)

Since 2022: Junior group leader @ FZ Jülich (Germany)

2023-2026: Research group leader @ University of Regensburg (Germany)

Since 2026: Professor for Theoretical Physics @ University of Regensburg (Germany)