  The use of Synthetic Aperture Radar (SAR) has greatly advanced our capacity
for comprehensive Earth monitoring, providing detailed insights into
terrestrial surface use and cover regardless of weather conditions, and at any
time of day or night. However, SAR imagery quality is often compromised by
speckle, a granular disturbance that poses challenges in producing accurate
results without suitable data processing. In this context, the present paper
explores the cutting-edge application of Quantum Machine Learning (QML) in
speckle filtering, harnessing quantum algorithms to address computational
complexities. We introduce here QSpeckleFilter, a novel QML model for SAR
speckle filtering. The proposed method compared to a previous work from the
same authors showcases its superior performance in terms of Peak
Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) on
a testing dataset, and it opens new avenues for Earth Observation (EO)
applications.
