Matrix product state recursion methods for strongly correlated quantum systems

TL;DR

This paper introduces a recursion-based method to improve the extrapolation of real-time dynamical correlation functions in matrix product state calculations, enhancing spectral function accuracy for strongly correlated systems.

Contribution

It proposes a novel recursion method that outperforms linear prediction, especially at high interaction strengths, and demonstrates its effectiveness on the Hubbard two-leg ladder.

Findings

Recursion method is exact for noninteracting Fermi systems.

It is more robust than linear prediction at large interactions.

Provides more accurate spectral functions for the Hubbard ladder.

Abstract

We present a method for extrapolation of real-time dynamical correlation functions which can improve the capability of matrix product state methods to compute spectral functions. Unlike the widely used linear prediction method, which ignores the origin of the data being extrapolated, our recursion methods utilize a representation of the wavefunction in terms of an expansion of the same wavefunction and its translations at earlier times. This recursion method is exact for a noninteracting Fermi system. Surprisingly, the recursion method is also more robust than linear prediction at large interaction strength. We test this method on the Hubbard two-leg ladder and present more accurate results for the spectral function than previous studies.