GEM: An implementation of the ghost-Gutzwiller approximation for simulating interacting quantum systems
Year: 2026
Type: preprint
Abstract: We present GEM (Ghost Embedding Method), an open-source software package written in Python for computing equilibrium properties of strongly correlated electronic systems within the ghost-Gutzwiller approximation method. GEM provides a computationally efficient framework for studying multi-orbital lattice models. It supports zero- and finite-temperature calculations and symmetry broken phases. It is integrated with the TRIQS ecosystem, providing tools for model construction, self-consistent solution, and evaluation of physical observables. We first detail the method's theoretical formulation, then we present the software architecture, and finally we introduce some practical workflow, which also validates the implementation against established results. In particular, we illustrate the capabilities of GEM through multiorbital and finite-temperature applications and discuss its computational cost relative to more demanding quantum embedding approaches.
Source: arXiv (Cornell University)
Institutions: Flatiron Institute, National Chung Cheng University, National Center for Theoretical Sciences, National Center for Theoretical Sciences, Physics Division, Iowa State University, Ames National Laboratory, Rochester Institute of Technology, Centre National de la Recherche Scientifique, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Université Paris-Saclay, Institut de Physique Théorique
Language: English
Subfield: Condensed Matter Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: G1286251239, G1418315740, G1519358529, G1866217153, G2316606464, G2327333655, G2441759041, G249831736, G285533474, G498139845, G524707868, LEAPS-MPS: Machine Learning the Variational Space for Efficient Quantum Embedding Simulations of Strongly Correlated Matter, G5959670421, G7626161611, G8943864594
OpenAlex ID: W7214717242