Quantum Information and Data Science for Modeling Classical Dynamics

Explore quantum information science and data-driven modeling of dynamical systems in this 90-minute talk by Dimitrios Giannakis from Dartmouth College. Delve into generalizations of operator-theoretic approaches using non-commutative algebras of operators. Learn about representations of observables and probability densities through multiplication and density operators, and how these concepts apply to data assimilation and dynamical closure problems. Discover structure-preserving computational schemes and their data-driven implementation using kernel methods. Examine applications in climate system modeling and multiscale systems, with a focus on the El Nino Southern Oscillation. Gain insights into the implementation of these methods on quantum computers. Presented as part of the DDPS (Data-Driven Physical Simulation) webinar series by Lawrence Livermore National Laboratory, this talk bridges the gap between quantum information, data science, and classical dynamics modeling.

DDPS | ‘Quantum information and data science for modeling classical dynamics’