Hybrid quantum tensor networks for aeroelastic applications

TL;DR

This paper explores hybrid quantum tensor networks combined with variational quantum circuits to address complex aeroelastic problems, demonstrating high accuracy in classification and promising results in regression tasks within quantum machine learning.

Contribution

It introduces an end-to-end trainable hybrid quantum tensor network algorithm tailored for aeroelastic applications, integrating tensor network encoding, dimensionality reduction, and variational quantum circuits.

Findings

High accuracy in binary classification tasks

Promising performance in regression of discrete variables

Highlights hyperparameter optimization challenges

Abstract

We investigate the application of hybrid quantum tensor networks to aeroelastic problems, harnessing the power of Quantum Machine Learning (QML). By combining tensor networks with variational quantum circuits, we demonstrate the potential of QML to tackle complex time series classification and regression tasks. Our results showcase the ability of hybrid quantum tensor networks to achieve high accuracy in binary classification. Furthermore, we observe promising performance in regressing discrete variables. While hyperparameter selection remains a challenge, requiring careful optimisation to unlock the full potential of these models, this work contributes significantly to the development of QML for solving intricate problems in aeroelasticity. We present an end-to-end trainable hybrid algorithm. We first encode time series into tensor networks to then utilise trainable tensor networks for dimensionality reduction, and convert the resulting tensor to a quantum circuit in the encoding step. Then, a tensor network inspired trainable variational quantum circuit is applied to solve either a classification or a multivariate or univariate regression task in the aeroelasticity domain.