Workshop IV: Monte Carlo and Machine Learning Approaches in Quantum Mechanics

May 23 - 27, 2022

Overview

Quantum mechanics has strong connections with probability theory and statistics. Quantum states are amenable to probabilistic interpretation based on laws of statistics. Many quantum problems can be reformulated in terms of Feynman’s path integral formulation, which amounts to computing quantum partition functions using statistical sampling techniques. In addition, new statistical learning approaches are emerging that aim to incorporate “quantumness” to ensure unitarity and long-range correlations that are so ubiquitous in quantum systems. Considering these recent developments, it appears timely to bring together the large community of people working on quantum systems and statistical techniques. This workshop will broadly address the reaches and limitations of statistics as applied to the modeling and understanding of quantum systems and highlight examples where quantum and statistical models enhance each other.

This workshop will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.

Organizing Committee

Kieron Burke (University of California, Irvine (UCI))
David Ceperley (University of Illinois at Urbana-Champaign)
Marivi Fernandez-Serra (SUNY Stony Brook)
Anatole von Lilienfeld (University of Basel)
Jonathan Weare (New York University, Mathematics)