Quantum Monte Carlo (QMC) methods have long been a powerful tool in computational quantum science. In recent years, the integration of neural networks into QMC has opened up exciting new possibilities. Neural network based QMC leverages the expressive power of neural networks, resulting in better representation of quantum states. In this talk, I will introduce the fundamentals of Variational Monte Carlo (VMC), a real-space QMC approach, and demonstrate how neural networks can improve its performance in solving complex many-body quantum problems.
Bio: Weiluo Ren is currently a research scientist at ByteDance Research, specializing in AI for Science. He holds a Ph.D. from Stanford University and a bachelor's degree from the University of Science and Technology of China. His research focuses on Neural Network based Quantum Monte Carlo methods and their applications in quantum physics and chemistry.