Computing the density of states for optical spectra by low-rank and QTT tensor approximation

TL;DR

This paper introduces a novel low-rank and QTT tensor approximation method to efficiently compute the density of states for large-scale optical spectra, especially in the context of the Bethe-Salpeter equation, reducing computational complexity.

Contribution

It presents a new interpolation scheme combining trace calculation and QTT tensor approximation to efficiently and accurately estimate the density of states for structured matrices.

Findings

QTT tensor rank weakly depends on system size

Accurate DOS recovery for problems with large spectral gaps

Requires logarithmic number of function calls relative to grid size

Abstract

In this paper, we introduce a new interpolation scheme to approximate the density of states (DOS) for a class of rank-structured matrices with application to the Tamm-Dancoff approximation (TDA) of the Bethe-Salpeter equation (BSE). The presented approach for approximating the DOS is based on two main techniques. First, we propose an economical method for calculating the traces of parametric matrix resolvents at interpolation points by taking advantage of the block-diagonal plus low-rank matrix structure described in [6, 3] for the BSE/TDA problem. Second, we show that a regularized or smoothed DOS discretized on a fine grid of size $N$ can be accurately represented by a low rank quantized tensor train (QTT) tensor that can be determined through a least squares fitting procedure. The latter provides good approximation properties for strictly oscillating DOS functions with multiple gaps, and requires asymptotically much fewer ($O(\log N)$) functional calls compared with the full grid size $N$. This approach allows us to overcome the computational difficulties of the traditional schemes by avoiding both the need of stochastic sampling and interpolation by problem independent functions like polynomials etc. Numerical tests indicate that the QTT approach yields accurate recovery of DOS associated with problems that contain relatively large spectral gaps. The QTT tensor rank only weakly depends on the size of a molecular system which paves the way for treating large-scale spectral problems.