Journal Article

FZJ-2026-02250

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Quantum Machine Learning for Earth Observation: A review and future prospects

Sebastianelli, A. (Corresponding author) ; Mauro, F. ; Delilbasic, A.FZJ* ; Stasio, P. D. ; Fan, F. ; Meoni, G. ; Cavallaro, G.FZJ* ; Zhu, X. X. ; Gamba, P. ; Ullo, S. L.

2026
IEEE New York, NY

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Please use a persistent id in citations: doi:10.1109/MGRS.2026.3676829

Abstract: Quantum machine learning (QML) is an emerging interdisciplinary field that employs quantum computing (QC) principles to address complex computational challenges. Traditional machine learning (ML) and deep learning (DL) models face major challenges in Earth observation (EO) due to the increasing volume and complexity of data. These challenges include computational intensity, energy consumption, and data management constraints. QML emerges as a promising paradigm for overcoming these barriers through quantum phenomena such as superposition, entanglement, and interference. This review aims at offering a detailed analysis of the state of the art and new trends in QML application to EO. This study methodically examines key advancements in QML for EO (QML4EO) through an exploration of the fundamental concepts of QML. The proposed review methodology involved systematic searches across prominent scientific databases, based on our extensive knowledge on the topic, using carefully formulated queries to ensure broad coverage and high relevance. In addition, this work addresses the changing institutional and geographic environment of QML4EO research, highlighting important centers of contribution and the important role of programs such as the IEEE Geoscience and Remote Sensing Society (GRSS) Quantum Earth Science and Technology (QUEST) Technical Committee. To transform QML from a specialized research area into a complementary and essential tool for advancing EO, this article critically analyzes current limitations and provides an outlook on future directions, highlighting the need for reliable hardware, improved algorithmic design, and standardized protocols.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. Simulation and Data Lab AI and ML for Remote Sensing (SDLRS)

Appears in the scientific report 2026

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Current Contents - Physical, Chemical and Earth Sciences ; Essential Science Indicators ; IF >= 10 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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