On the potentials of Tensor-based Quantum Machine Learning for SAR land-cover classification

Abstract

Synthetic Aperture Radar (SAR) data, characterised by its high-dimensionality and complex spatial correlations, poses significant challenges in terms of efficient processing and meaningful interpretation. Classical algorithms, while effective, often struggle with the sheer volume and intricacy of the data. This paper introduces a novel approach employing tensor quantum machine learning (QML) to tackle the intricacies of SAR data. By harnessing the computational advantages of quantum mechanics and the representational efficiency of tensor networks, we try to achieve enhanced feature extraction and pattern recognition. We look at various Tensor decomposition schemes to reduce data dimensionality as well as Tensor based quantum circuits to perform land-cover classification. Preliminary results, based on simulations, demonstrate the potential of our tensor QML framework. For the scope of this research so far, we worked with simulated amplitude and phase data, but we will be applying the same for real world data in the future. This interdisciplinary study not only opens avenues for improved SAR data analysis but also enriches the burgeoning field of quantum machine learning by highlighting its applicability in remote sensing domains.