Advances in Many-Body Theories: from First Principle Methods to Quantum Computing and Machine Learning
02 November 2020 — 06 November 2020
Quantum computing and machine learning are two of the most promising approaches for studying complex physical systems where several lengths and energy scales are involved. Traditional many-particle methods, either quantum mechanical or classical ones, face huge dimensionality problems when applied to studies of systems with many interacting particles. By bringing together experts from these fields, this workshop will explore the links between these exciting new approaches and traditional many-particle methods in order to map out future research paths.
The workshop will be held on the conference platform Zoom.
Morten Hjorth-Jensen (Michigan State University & University of Oslo)
David Jarvis Dean (Oak Ridge National Laboratory)
Thomas Papenbrock (University of Tennessee & Oak Ridge National Laboratory)
Martin Savage (INT & University of Washington)
Gaute Hagen (Oak Ridge National Laboratory/The University of Tennessee)
Stefano Gandolfi (Los Alamos National Laboratory)
Jason Holt (TRIUMF - Vancouver)
Registration no longer available.