Semistochastic Heat-bath Configuration Interaction method: selected configuration interaction with semistochastic perturbation theory
Sandeep Sharma, Adam Holmes, Guillaume Jeanmairet, Ali Alavi, and C. J. Umrigar

TL;DR
This paper introduces a semistochastic extension to the heat-bath configuration interaction (HCI) method, significantly reducing memory usage and increasing efficiency for large active space quantum chemistry calculations.
Contribution
The authors develop a semistochastic algorithm for perturbation theory within HCI, enabling calculations on larger systems with reduced memory and improved speed, without sign problems.
Findings
Able to compute correlation energies of large active spaces.
Achieved better than 1 mHa accuracy in seconds to minutes.
Demonstrated scalability on complex molecular systems.
Abstract
We extend the recently proposed heat-bath configuration interaction (HCI) method [Holmes, Tubman, Umrigar, J. Chem. Theory Comput. 12, 3674 (2016)], by introducing a semistochastic algorithm for performing multireference Epstein-Nesbet perturbation theory, in order to completely eliminate the severe memory bottleneck of the original method. The proposed algorithm has several attractive features. First, there is no sign problem that plagues several quantum Monte Carlo methods. Second, instead of using Metropolis-Hastings sampling, we use the Alias method to directly sample determinants from the reference wavefunction, thus avoiding correlations between consecutive samples. Third, in addition to removing the memory bottleneck, semistochastic HCI (SHCI) is faster than the deterministic variant for many systems if a stochastic error of 0.1 mHa is acceptable. Fourth, within the SHCI…
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