Quantum Machine Learning for Earth Observation Data Analysis

The analysis of Earth observation (EO) data has become a significant computational challenge due to the increasing amount of data available. A new study explores the application of Quantum Machine Learning (QML) to address this challenge, proposing a hybrid model that combines multitask learning techniques with quantum convolution operations.

The model aims to improve the efficiency of data encoding and the extraction of relevant features for classification. Preliminary results, obtained using several EO data benchmarks, suggest the potential of QML in this field, despite the current limitations of quantum devices.

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