28 citations · 31 across the 4 of their papers we have counts for
4 papers
Linear Foundation Model for Quantum Embedding: Data-Driven Compression of the Ghost Gutzwiller Variational Space
Samuele Giuli, Hasanat Hasan, Benedikt Kloss +5
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 t…
Active Learning approach to simulations of Strongly Correlated Matter with the Ghost Gutzwiller Approximation
Marius S. Frank, Denis G. Artiukhin, Tsung-Han Lee +4
Quantum embedding (QE) methods such as the Ghost Gutzwiller Approximation (gGA) offer a powerful approach to simulating strongly-correlated systems, but come with the computational…
Error mitigation in variational quantum eigensolvers using tailored probabilistic machine learning
Tao Jiang, John Rogers, Marius S. Frank +3
Quantum computing technology has the potential to revolutionize the simulation of materials and molecules in the near future. A primary challenge in achieving near-term quantum adv…
Quantum-embedding description of the Anderson lattice model with the ghost Gutzwiller Approximation
Marius S. Frank, Tsung-Han Lee, Gargee Bhattacharyya +5
We present benchmark calculations of the Anderson lattice model based on the recently-developed "ghost Gutzwiller approximation". Our analysis shows that, in some parameters regime…