A Perturbative Density Matrix Renormalization Group Algorithm for Large Active Spaces

TL;DR

The paper introduces p-DMRG, a perturbative approach that combines low-cost DMRG with second-order perturbation theory, enabling efficient calculations for large active spaces in quantum chemistry.

Contribution

It presents a novel perturbative DMRG method that reduces computational cost for large active spaces by combining a low-bond-dimension DMRG with perturbation theory and MPS expansions.

Findings

p-DMRG achieves energies comparable to high-bond-dimension variational DMRG.

The method significantly lowers computational cost and memory requirements.

Numerical tests on Cr2 and butadiene demonstrate its effectiveness.

Abstract

We describe a low cost alternative to the standard variational DMRG (density matrix renormalization group) algorithm that is analogous to the combination of selected configuration interaction plus perturbation theory (SCI+PT). We denote the resulting method p-DMRG (perturbative DMRG) to distinguish it from the standard variational DMRG. p-DMRG is expected to be useful for systems with very large active spaces, for which variational DMRG becomes too expensive. Similar to SCI+PT, in p-DMRG a zeroth-order wavefunction is first obtained by a standard DMRG calculation, but with a small bond dimension. Then, the residual correlation is recovered by a second-order perturbative treatment. We discuss the choice of partitioning for the perturbation theory, which is crucial for its accuracy and robustness. To circumvent the problem of a large bond dimension in the first-order wavefunction, we use a sum of matrix product states (MPS) to expand the first-order wavefunction, yielding substantial savings in computational cost and memory. We also propose extrapolation schemes to reduce the errors in the zeroth- and first-order wavefunctions. Numerical results for Cr 2 with a (28e,76o) active space and 1,3-butadiene with a (22e,82o) active space reveal that p-DMRG provides ground state energies of a similar quality to variational DMRG with very large bond dimensions, but at a significantly lower computational cost. This suggests that p-DMRG will be an efficient tool for benchmark studies in the future.