  In the pursuit of accurate descriptions of strongly correlated quantum
many-body systems, dynamical mean-field theory (DMFT) has been an invaluable
tool for elucidating the spectral properties and quantum phases of both
phenomenological models and ab initio descriptions of real materials. Key to
the DMFT process is the self-consistent map of the original system into an
Anderson impurity model, the ground state of which is computed using an
impurity solver. The power of the method is thus limited by the complexity of
the impurity model the solver can handle. Simulating realistic systems
generally requires many correlated sites. By adapting the recently proposed
adaptive sampling configuration interaction (ASCI) method as an impurity
solver, we enable much more efficient zero temperature DMFT simulations. The
key feature of the ASCI method is that it selects only the most relevant
Hilbert space degrees of freedom to describe the ground state. This reduces the
numerical complexity of the calculation, which will allow us to pursue future
DMFT simulations with more correlated impurity sites than in previous works.
Here we present the ASCI-DMFT method and example calculations on the
one-dimensional and two-dimensional Hubbard models that exemplify its efficient
convergence and timing properties. We show that the ASCI approach is several
orders of magnitude faster than the current best published ground state DMFT
simulations, which allows us to study the bath discretization error in
simulations with small clusters, as well as to address cluster sizes beyond the
current state of the art. Our approach can also be adapted for other embedding
methods such as density matrix embedding theory and self-energy embedding
theory.
