Physics Meets AI: Quantum Computing and Generative Modeling

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Abstract
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Generative AI is rapidly expanding beyond text, image, and code generation to scientific applications such as drug discovery and materials design. At its core, generative learning is the problem of modeling complex high-dimensional probability distributions and generating useful samples from them. This perspective naturally connects modern AI with statistical physics and quantum computing. In this talk, I will discuss this connection in two complementary directions. First, I will introduce how quantum computing can support generative learning through energy-based models, focusing on Boltzmann machines and their relation to quantum annealing. I will also discuss how this perspective can be connected to modern generative AI architectures. Second, I will discuss how generative learning can support quantum simulation. In particular, I will discuss how generative models can learn quantum measurement distributions and help estimate ground-state energies with fewer direct quantum-circuit measurements. Together, these examples illustrate a bidirectional relationship between generative AI and quantum computing: quantum devices as sampling resources for AI, and generative models as computational tools for quantum simulation.

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About the Speaker
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Daniel K. Park is an Associate Professor at Yonsei University in Korea, where he conducts research in quantum machine learning, quantum simulation, and quantum error correction. He also serves as Vice Director of the Institute of Quantum Information Technology at Yonsei University. Before joining Yonsei University, he held research positions at KAIST and Sungkyunkwan University. He received his Ph.D. in Physics from the University of Waterloo, specializing in quantum information at the Institute for Quantum Computing.