Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Quantum Machine Learning: Earth Observation Challenges

Mihai P. Datcu

Published Oct 1, 2018• 0 citations
OpenAlex

Related papers

Exploring Quantum Machine Learning’s Role in Earth Observation Data Classification

Fan Fan, Xiao Xiang Zhu, Technische Universität München

•Jan 1, 2025•0 citations

Quo Vadis, Quantum Machine Learning?: Quantum kernel methods meet Earth observation

Artur Miroszewski, Jakub Nalepa, Agata M. Wijata +3 more

IEEE Geoscience and Remote Sensing Magazine•Jan 21, 2026•2 citations

NASA Nearest Earth Object Classification Using Quantum Machine Learning: A Survey

Aman Singh Bhogal, Mausmi Sinha, Pratiksha Meshram

Lecture notes in electrical engineering•Jan 1, 2024•3 citations

Quantum Machine Learning for Earth Observation: A review and future prospects

Alessandro Sebastianelli, Francesco Mauro, Amer Delilbasic +7 more

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.

IEEE Geoscience and Remote Sensing Magazine•Apr 13, 2026•3 citations

Quantum machine learning: Challenges and Opportunities

Leonard Wossnig, Simone Severini B

Bulletin of the American Physical Society•Mar 6, 2019•1 citation

Multitask Learning for Earth Observation Data Classification with Hybrid Quantum Network

Fan Fan, Yilei Shi, Tobias Guggemos +1 more

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.

arXiv:2601.22195•Jan 29, 2026•0 citations
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.
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.