An efficient stochastic algorithm for the perturbative density matrix renormalization group in large active spaces

TL;DR

This paper introduces a stochastic algorithm for the perturbative density matrix renormalization group method, significantly improving computational efficiency and accuracy for large active space quantum chemistry calculations.

Contribution

The paper presents a stochastic implementation of p-DMRG that overcomes previous computational bottlenecks, enabling more efficient large active space simulations.

Findings

Demonstrates efficiency on C₂ and Cr₂ benchmarks

Achieves accurate results with reduced computational cost

Validates the stochastic approach as a viable alternative

Abstract

We present an efficient stochastic algorithm for the recently introduced perturbative density matrix renormalization group (p-DMRG) method for large active spaces. The stochastic implementation bypasses the computational bottleneck involved in solving the first order equation in the earlier deterministic algorithm. We demonstrate the efficiency and accuracy of the algorithm on the C$_2$ and Cr$_2$ molecular benchmark systems.