Large Scale Parallelization in Stochastic Coupled Cluster

TL;DR

This paper introduces a highly parallelizable stochastic coupled cluster algorithm that improves scalability and efficiency for large quantum systems, demonstrated on electron gas and water dimer models.

Contribution

It presents a novel parallel stochastic coupled cluster method with improved sampling techniques, enhancing scalability and efficiency over previous algorithms.

Findings

Algorithm performs well on electron gas and water dimer systems.

Introduces full non-composite and multi-spawn sampling improvements.

Achieves scalable results at various coupled cluster truncation levels.

Abstract

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.