Quantum Machine Learning for Earth Observation: A Review and Future Prospects

Quantum Machine Learning (QML) is an emerging interdisciplinary field that leverages quantum computing 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 by harnessing 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. We methodically examine important advancements in the field of QML4EO, by exploring the fundamental ideas of QML. Our 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, we address the changing institutional and geographic environment of QML4EO research, highlighting important centers of contribution and the important role of programs such as the IEEE GRSS QUEST Technical Committee. In order to transform QML from a specialized research area into a complementary and essential tool for advancing EO, this paper critically analyzes current limitations and provides an outlook on future directions, highlighting the need for reliable hardware, improved algorithmic design, and standardized protocols.