Machine Learning for Quantum Science

Machine learning has expanded our ability to model complex quantum systems by learning relationships between measurements, material properties, and dynamics. Yet translating these relationships into a physical understanding remains an open question. Gaining physical insight requires connecting patterns in the data to the underlying processes and principles that generate them. To this end, we develop AI/ML methods to identify the physical processes consistent with data and determine the level of theoretical description required to explain them. Applications include energy transport in quantum materials, light-matter interactions and dissipative dynamics.

"It is nice to know that the computer understands the problem, but I would like to understand it too" -- E. Wigner