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Quantum embedding (QE) methods such as the Ghost Gutzwiller Approximation(gGA) offer a powerful approach to simulating strongly-correlated systems, butcome with the computational bottleneck of computing the ground state of anauxiliary embedding Hamiltonian (EH) iteratively. In this work, we introduce anactive learning (AL) framework integrated within the gGA to address thischallenge. The methodology is applied to the single-band Hubbard model andresults in a significant reduction in the number of instances where the EH mustbe solved. Through a principal component analysis (PCA), we find that the EHparameters form a low-dimensional structure that is largely independent of thegeometric specifics of the systems, especially in the strongly-correlatedregime. Our AL strategy enables us to discover this low-dimensionalitystructure on the fly, while leveraging it for reducing the computational costof gGA, laying the groundwork for more efficient simulations of complexstrongly-correlated materials.

Active Learning approach to simulations of Strongly Correlated Matter with the Ghost Gutzwiller Approximation