Hybrid Quantum Deep Learning With Superpixel Encoding for Earth Observation Data Classification

Autor(en)

Fan Fan, Yilei Shi, Tobias Guggemos, Xiao Xiang Zhu

Abstrakt

Earth observation (EO) has inevitably entered the Big Data era. The computational challenge associated with analyzing large EO data using sophisticated deep learning models has become a significant bottleneck. To address this challenge, there has been a growing interest in exploring quantum computing as a potential solution. However, the process of encoding EO data into quantum states for analysis potentially undermines the efficiency advantages gained from quantum computing. This article introduces a hybrid quantum deep learning model that effectively encodes and analyzes EO data for classification tasks. The proposed model uses an efficient encoding approach called superpixel encoding, which reduces the quantum resources required for large image representation by incorporating the concept of superpixels. To validate the effectiveness of our model, we conducted evaluations on multiple EO benchmarks, including Overhead-MNIST, So2Sat LCZ42, and SAT-6 datasets. In addition, we studied the impacts of different interaction gates and measurements on classification performance to guide model optimization. The experimental results suggest the validity of our model for accurate classification of EO data. Our models and code are available on github.com/zhu-xlab/SEQNN.

Organisation(en)

Quantenoptik, Quantennanophysik und Quanteninformation

Externe Organisation(en)

Technische Universität München, Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Munich Center for Machine Learning (MCML)

Journal

IEEE transactions on neural networks and learning systems

Band

36

Seiten

11271-11284

Anzahl der Seiten

14

ISSN

2162-237X

DOI

https://doi.org/10.1109/TNNLS.2024.3518108

Publikationsdatum

01-2025

Peer-reviewed

Ja

ÖFOS 2012

102040 Quantencomputing, 102019 Machine Learning

Schlagwörter

ASJC Scopus Sachgebiete

Software, Computer Science Applications, Computer Networks and Communications, Artificial Intelligence

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/c508b00e-ec51-435e-9973-ec27a7cd28e5