Coupled cluster theory is a vital cornerstone of electronic structure theory and is being applied to ever-larger systems. Stochastic approaches to quantum chemistry have grown in importance and offer compelling advantages over traditional deterministic algorithms in terms of computational demands, theoretical flexibility or lower scaling with system size. We present a highly parallelizable algorithm of the coupled cluster Monte Carlo method involving sampling of clusters of excitors over multiple time steps. The behaviour of the algorithm is investigated on the uniform electron gas and the water dimer at CCSD, CCSDT and CCSDTQ levels. We also describe two improvements to the original sampling algorithm, full non-composite and multi-spawn sampling. A stochastic approach to coupled cluster results in an efficient and scalable implementation at arbitrary truncation levels in the coupled cluster expansion.Thomas Young Centre TYC-101,
EPSRC Centre for Doctoral Training in Computational Methods for Materials Science EP/L015552/1,
Cambridge Philosophical Society,
EPSRC,
CHESS,
Royal Society University Research Fellowship UF110161 and UF160398,
ARCHER UK National Supercomputing Service (http://www.archer.ac.uk) grant e507,
UK Research Data Facility (http://www.archer.ac.uk/documentation/rdf-guide) grant e507,
resources provided by the Cambridge Service for Data Driven
Discovery (CSD3) operated by the University of Cambridge Research Computing Service (http://www.csd3.cam.ac.uk/), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sci-
ences Research Council (capital grant EP/P020259/1),
and DiRAC funding from the Science and Technology
Facilities Council (www.dirac.ac.uk
