# Stochastic Coupled Cluster Theory: Efficient Sampling of the Coupled Cluster Expansion

## TL;DR

This paper introduces new stochastic sampling methods for coupled cluster theory that significantly improve stability and reduce computational costs by selectively sampling excitation levels, especially at higher truncations.

## Contribution

The paper proposes innovative sampling strategies, even and truncated selection, for stochastic coupled cluster theory, enhancing efficiency and stability over previous methods.

## Key findings

- Reduced sampling of terms by 77% at CCSDT
- Achieved 98% reduction at CCSDTQ56
- Demonstrated improved calculation stability

## Abstract

We consider the sampling of the coupled cluster expansion within stochastic coupled cluster theory. Observing the limitations of previous approaches due to the inherently non-linear behaviour of a coupled cluster wavefunction representation we propose new approaches based upon an intuitive, well-defined condition for sampling weights and on sampling the expansion in cluster operators of different excitation levels. We term these modifications even and truncated selection respectively. Utilising both approaches demonstrates dramatically improved calculation stability as well as reduced computational and memory costs. These modifications are particularly effective at higher truncation levels owing to the large number of terms within the cluster expansion that can be neglected, as demonstrated by the reduction of the number of terms to be sampled at the level of CCSDT by 77% and at CCSDTQ56 by 98%.