  We propose a modified coupled cluster Monte Carlo algorithm that
stochastically samples connected terms within the truncated
Baker--Campbell--Hausdorff expansion of the similarity transformed Hamiltonian
by construction of coupled cluster diagrams on the fly. Our new approach --
diagCCMC -- allows propagation to be performed using only the connected
components of the similarity-transformed Hamiltonian, greatly reducing the
memory cost associated with the stochastic solution of the coupled cluster
equations. We show that for perfectly local, noninteracting systems, diagCCMC
is able to represent the coupled cluster wavefunction with a memory cost that
scales linearly with system size. The favorable memory cost is observed with
the only assumption of fixed stochastic granularity and is valid for arbitrary
levels of coupled cluster theory. Significant reduction in memory cost is also
shown to smoothly appear with dissociation of a finite chain of helium atoms.
This approach is also shown not to break down in the presence of strong
correlation through the example of a stretched nitrogen molecule. Our novel
methodology moves the theoretical basis of coupled cluster Monte Carlo closer
to deterministic approaches.
