报告题目：Machine Learning meets Quantum Many-body Physics

报 告 人: 罗迪，麻省理工学院和哈佛物理博后

报告时间：2024年2月18日（周日）上午9：00

报告方式：腾讯会议ID:960-119-871

报告摘要：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.

报告人简介：罗迪，麻省理工学院和哈佛物理博士后与AI Institute for Artificial Intelligence and Fundamental Interactions 研究员（IAIFI Fellow）。他2016年在香港大学获得物理和数学双学士学位，2021年从伊利诺伊大学香槟分校UIUC获得数学硕士和物理博士。他的研究兴趣为AI+Science和量子计算，包括发展AI和量子算法用于量子物态，高能物理和量子信息的科学模拟和发现，以及利用量子物理和统计物理发展构建AI理论和模型。