Auto Quantum Machine Learning for Multisource Classification

TL;DR

This paper introduces an automated quantum machine learning approach for multisource data fusion, demonstrating improved change detection accuracy over previous quantum methods using a multispectral dataset.

Contribution

The paper presents an automated QML framework for data fusion, comparing its performance with classical and manually designed QML models in remote sensing applications.

Findings

AQML outperforms classical MLPs in multisource classification tasks.

AQML achieves higher accuracy in change detection on the ONERA dataset.

Automated QML offers a promising approach for complex data fusion challenges.

Abstract

With fault-tolerant quantum computing on the horizon, there is growing interest in applying quantum computational methods to data-intensive scientific fields like remote sensing. Quantum machine learning (QML) has already demonstrated potential for such demanding tasks. One area of particular focus is quantum data fusion -- a complex data analysis problem that has attracted significant recent attention. In this work, we introduce an automated QML (AQML) approach for addressing data fusion challenges. We evaluate how AQML-generated quantum circuits perform compared to classical multilayer perceptrons (MLPs) and manually designed QML models when processing multisource inputs. Furthermore, we apply our method to change detection using the multispectral ONERA dataset, achieving improved accuracy over previously reported QML-based change detection results.