Zero-site DMRG and the optimal low-rank correction

TL;DR

This paper introduces a zero-site DMRG algorithm optimizing message tensors between sites, along with low-rank corrections to improve convergence and avoid local minima, demonstrated on spin chains and electron systems.

Contribution

It proposes a novel zero-site DMRG method that separates optimization and decimation, and introduces low-rank perturbations to enhance convergence without parameter tuning.

Findings

Achieves similar convergence rates to existing methods with lower computational cost.

Effective in systems with many degrees of freedom per site.

Successfully tested on Heisenberg spin chains and free electron models.

Abstract

A zero-site density matrix renormalization algorithm (DMRG0) is proposed to minimize the energy of matrix product states (MPS). Instead of the site tensors themselves, we propose to optimize sequentially the "message" tensors between neighbor sites, which contain the singular values of the bipartition. This leads to a local minimization step that is independent of the physical dimension of the site. Conceptually, it separates the optimization and decimation steps in DMRG. Furthermore, we introduce two new global perturbations based on the optimal low-rank correction to the current state, which are used to avoid local minima. They are determined variationally as the MPS closest to the one-step correction of the Lanczos or Jacobi-Davidson eigensolver, respectively. These perturbations mainly decrease the energy and are free of hand-tuned parameters. Compared to existing single-site enrichment proposals, our approach gives similar convergence ratios per sweep while the computations are cheaper by construction. Our methods may be useful in systems with many physical degrees of freedom per lattice site. We test our approach on the periodic Heisenberg spin chain for various spins, and on free electrons on the lattice.