Artificial Intelligence Meets Quantum Many-body Physics

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 lattice gauge theories. The advancement of AI provides an emerging unifying approach to capture strongly correlated and topological phases in both condensed matter and high energy physics. I will conclude with a discussion on the new possibilities of AI for physics, as well as how physics theories can help advance AI.