The simulation of quantum many-body physics, pivotal in uncovering ground state properties and real-time dynamics, is essential in the study of quantum science. In this talk, I will focus on how neural network quantum states, enriched with symmetries and physics principles, provide new opportunities for tackling challenges in quantum many-body simulations. I will introduce the pioneering work of designing anti-symmetric and gauge equivariant neural wavefunctions, which provides new tools for exploring exotic phases of quantum matter in two-dimensional quantum materials and quantum gauge theories. Furthermore, I will discuss how neural network generative models can be used to simulate non-equilibrium quantum dynamics based on quantum information theory, and applied in quantum experiments and computation. I will conclude with a discussion on the new possibilities of AI for physics, as well as how physics theories can help advance AI.
 

Di Luo is a postdoc associate at the Massachusetts Institute of Technology and Harvard University, and holds the position of IAIFI Fellow at the NSF AI Institute for Artificial Intelligence and Fundamental Interactions. He received his undergraduate degree with majors in physics and mathematics from the University of Hong Kong in 2016. He graduated with a MS in mathematics and a PhD in physics at the University of Illinois, Urbana-Champaign in 2021. Di Luo's research is centered on the development of machine learning approaches and quantum algorithms within the realms of condensed matter physics, high energy physics, and quantum information science. He is interested in advancing the study of AI for Physics as well as Physics for AI.

To request for Zoom information, please write to phweb@ust.hk.