Linear Foundation Model for Quantum Embedding: Data-Driven Compression of the Ghost Gutzwiller Variational Space

TL;DR

This paper introduces a data-driven linear foundation model using PCA to compress quantum embedding spaces, significantly reducing computational costs in simulating strongly correlated materials.

Contribution

The authors develop a PCA-based compression method for quantum embedding that is transferable across different lattice types and validated on complex materials like plutonium.

Findings

Transferable variational space reduces computational cost

Single variational space reproduces energetics across phases

Order-of-magnitude speedup in quantum embedding calculations

Abstract

Simulations of quantum matter rely mainly on Kohn-Sham density functional theory (DFT), which often fails for strongly correlated systems. Quantum embedding (QE) theories address this limitation by mapping the system onto an auxiliary embedding Hamiltonian (EH) describing fragment-environment interactions, but the EH is typically large and its iterative solution is the primary computational bottleneck. We introduce a linear foundation model for QE that utilizes principal component analysis (PCA) to compress the space of quantum states needed to solve the EH within a small variational subspace. Using a data-driven active-learning scheme, we learn this subspace from EH ground states and reduce each embedding solve to a deterministic ground-state eigenvalue problem in the reduced space. Within the ghost Gutzwiller approximation (ghost-GA), we show for a three-orbital Hubbard model that a variational space learned on a Bethe lattice is transferable to square and cubic lattices without additional training, while substantially reducing the cost of the EH step. We further validate the approach on plutonium, where a single variational space reproduces the energetics of all six crystalline phases while reducing the cost of the EH solution by orders of magnitude. This provides a practical route to overcome the main computational bottleneck of QE frameworks, paving the way for high-throughput ab initio simulations of strongly correlated materials at a near-DFT cost.