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High-quality excitation generators are crucial to the effectiveness ofCoupled cluster Monte Carlo (CCMC) and full configuration interaction QuantumMonte Carlo (FCIQMC) calculations. The heat bath sampling of Holmes et al. [A.A. Holmes, H. J. Changlani, and C. J. Umrigar, J. Chem. Theory Comput. 12, 1561(2016)] dramatically increases the efficiency of the spawn step of suchalgorithms but requires memory storage scaling quartically with system sizewhich can be prohibitive for large systems. Alavi et al. [S. D. Smart, G. H.Booth, and A. Alavi, unpublished] then approximated these weights with weightsbased on Cauchy--Schwarz-like inequalities calculated on-the-fly. Whilereducing the memory cost, this algorithm scales linearly in system sizecomputationally. We combine both these ideas with the single reference natureof many systems, and introduce a spawn-sampling algorithm that has low memoryrequirements (quadratic in basis set size) compared to the heat bath algorithmand only scales either independently of system size (CCMC) or linearly in thenumber of electrons (FCIQMC). On small water chains with localized orbitals, weshow that it is equally efficient as the other excitation generators. As thesystem gets larger, it converges faster than the on-the-fly weight algorithm,while having a much more favourable memory scaling than the heat bathalgorithm.

Exciting determinants in Quantum Monte Carlo: Loading the dice with fast, low memory weights