  The multi-reference coupled-cluster Monte Carlo (MR-CCMC) algorithm is a
determinant-based quantum Monte Carlo (QMC) algorithm that is conceptually
similar to Full Configuration Interaction QMC (FCIQMC). It has been shown to
offer a balanced treatment of both static and dynamic correlation while
retaining polynomial scaling, although application to large systems with
significant strong correlation remained impractical. In this paper, we document
recent algorithmic advances that enable rapid convergence and a more black-box
approach to the multi-reference problem. These include a logarithmically
scaling metric-tree based excitation acceptance algorithm to search for
determinants connected to the reference space at the desired excitation level
and a symmetry-screening procedure for the reference space. We show that, for
moderately sized reference spaces, the new search algorithm brings about an
approximately 8-fold acceleration of one MR-CCMC iteration, while the symmetry
screening procedure reduces the number of active reference space determinants
at essentially no loss of accuracy. We also introduce a stochastic
implementation of an approximate wall projector, which is the infinite
imaginary time limit of the exponential projector, using a truncated expansion
of the wall function in Chebyshev polynomials. Notably, this wall-Chebyshev
projector can be used to accelerate any projector-based QMC algorithm. We show
that it requires significantly fewer applications of the Hamiltonian to achieve
the same statistical convergence. We benchmark these acceleration methods on
the beryllium and carbon dimers, using initiator FCIQMC and MR-CCMC with basis
sets up to cc-pVQZ quality.
