Quantum Monte Carlo Approaches for Correlated Systems

By F. Becca. 2017

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Provably efficient machine learning for quantum many-body problems (2021, background): “Powerfulmethods,such asden-sity functional theory ( 1 , 2 ), quantum Monte Carlo ( 3 – 5 ), and density-matrix renormalization group ( 6 , 7 ), have enabled solutions to certain restricted instances of many-body problems, but many general classes of problems remain outside the reach of even the…”
Thermodynamics (2021, unknown): “While QMC can be applied to problems in any dimension, it suffers from a sign ∗ sirker@physics.umanitoba.ca problem in the sampling weights for fermionic models and frustrated spin models [3, 4].”
Machine learning and the physical sciences (2019, methodology): “The stochastic reconfiguration (SR) approach (Sorella, 1998; Becca and Sorella, 2017) and its generalization to the time-dependent case (Carleo et al.”