Multitask Learning for Earth Observation Data Classification with Hybrid Quantum Network

TL;DR

This paper introduces a hybrid quantum-classical multitask learning model for Earth observation data classification, demonstrating its potential advantages and effectiveness on multiple benchmarks despite current quantum device limitations.

Contribution

It proposes a novel hybrid quantum neural network with multitask learning and location weight modules for improved EO data classification.

Findings

Model achieves competitive accuracy on EO benchmarks

Quantum features enhance classification performance

Explores factors contributing to quantum advantage

Abstract

Quantum machine learning (QML) has gained increasing attention as a potential solution to address the challenges of computation requirements in the future. Earth observation (EO) has entered the era of Big Data, and the computational demands for effectively analyzing large EO data with complex deep learning models have become a bottleneck. Motivated by this, we aim to leverage quantum computing for EO data classification and explore its advantages despite the current limitations of quantum devices. This paper presents a hybrid model that incorporates multitask learning to assist efficient data encoding and employs a location weight module with quantum convolution operations to extract valid features for classification. The validity of our proposed model was evaluated using multiple EO benchmarks. Additionally, we experimentally explored the generalizability of our model and investigated the factors contributing to its advantage, highlighting the potential of QML in EO data analysis.