Quantum impurity solvers are the computational bottleneck of quantum embedding approaches to correlated materials, such as dynamical mean-field theory (DMFT). We show that neural networks trained on synthetic, material-agnostic data learn the impurity mapping from hybridization functions and local interactions to Green's functions with quantitative accuracy for both model systems and real materials, providing fast solvers for single- and multi-orbital models. Benchmarks against numerically controlled quantum Monte Carlo show that the method reproduces the Mott transition, multi-orbital phase diagrams of Hubbard-Kanamori models, and the electronic properties of SrVO3 and SrMnO3. The learned solvers achieve orders-of-magnitude speedup and can initialize controlled calculations, dramatically accelerating DMFT while preserving accuracy.
@article{arxiv.2603.15741,
title = {Neural-Network Quantum Embedding Solvers for Correlated Materials},
author = {Agnes Valenti and Ina Park and Antoine Georges and Andrew J. Millis and Olivier Parcollet},
journal= {arXiv preprint arXiv:2603.15741},
year = {2026}
}