AI + Quantum: Machine Learning Meets Quantum Science

Abstract:
The discovery of novel quantum materials and the advancement in quantum computation are driving revolutionary changes within the realms of quantum science and technology. In this talk, I will first present how generative models for quantum states, enriched with symmetries and physics principles, opens up novel avenues for addressing the challenges posed by high-dimensional simulation of quantum systems. It offers new opportunities to the study of high energy physics, quantum materials, quantum information and computation. Meanwhile, I will discuss recent explorations for developing machine learning and robotics methods powered by physics and quantum theories. This includes the development of a family of generative models grounded in various physical processes, the creation of efficient neural network solvers for generic PDEs inspired by quantum principles, and the optimization of multi-legged robots’ locomotion through many-body physics theory.

Bio:
Di Luo is an assistant professor of electrical and computer engineering at UCLA and affiliated with the Center for Quantum Science and Engineering. Prior to joining UCLA, he was an IAIFI fellow at the NSF AI Institute for Artificial Intelligence and Fundamental Interactions, affiliated with MIT Center for Theoretical Physics and Harvard Physics Department. He graduated with a master degree in mathematics and a Ph.D. degree in physics at the University of Illinois Urbana-Champaign in 2021. He received his undergraduate degree with double majors in physics and mathematics from the University of Hong Kong in 2016. Di Luo has held internships with Google Quantum AI, the Flatiron Institute of the Simons Foundation and the Vector Institute for Artificial Intelligence. Di Luo’s research explores areas in AI + Science and Quantum Computation, focusing on developing machine learning and quantum algorithms for discoveries and advancements in science and engineering, as well as designing new theories and algorithms for AI and robotics inspired by mathematics and physics.