Kernel ridge regression predicts the self-energy of 1D Hubbard models from static and dynamic mean-field features, enabling Green's functions via Dyson's equation for U/t from weak to strong coupling.
Solving the quantum many-body problem with artificial neural net- works.Science, 355(6325):602–606, 2017
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2025 2representative citing papers
Global Annealing Monte Carlo with ML global moves plus local updates outperforms Simulated Annealing and is more robust than Population Annealing on 3D Ising spin glasses without hyperparameter tuning.
citing papers explorer
-
Machine Learning Green's Functions of Strongly Correlated Hubbard Models
Kernel ridge regression predicts the self-energy of 1D Hubbard models from static and dynamic mean-field features, enabling Green's functions via Dyson's equation for U/t from weak to strong coupling.
-
Demonstrating Real Advantage of Machine-Learning-Enhanced Monte Carlo for Combinatorial Optimization
Global Annealing Monte Carlo with ML global moves plus local updates outperforms Simulated Annealing and is more robust than Population Annealing on 3D Ising spin glasses without hyperparameter tuning.