Tensor Network States for Vibrational Spectroscopy

TL;DR

This paper reviews tensor network representations, especially matrix product states, for modeling quantum vibrational states, highlighting their advantages and potential in vibrational spectroscopy.

Contribution

It introduces the application of matrix product state decomposition and DMRG optimization to vibrational quantum states, advancing computational methods in spectroscopy.

Findings

Matrix product states effectively model vibrational quantum states.

DMRG optimization improves the accuracy of tensor network representations.

Tensor networks offer promising prospects for vibrational spectroscopy applications.

Abstract

This review elaborates on the foundation, the advantages, and the prospects of tensor network representations for quantum states in vibrational spectroscopy. The focus is on the recently introduced matrix product state decomposition of nuclear quantum states and its optimization by the density matrix renormalization group algorithm.