In this context the presentation will focus on relevant aspects of quantum technologies for image understnading. With the goal to identify if a quantum algorithm may bring any advantage compared with classical methods, will be firstly analysed the data complexity (i.e. data as prediction advantage). Secondly, it will be presented the classes of complexity of the algorithms. Thirdly, it will be identify major challenges in EO which could not yet be solved by classical methods, as for instance the causality analysis.

Data embedding is of key importance. Non-quantum data are many times “artificially” encoded at the input of quantum computers, thus quantum algorithms may not be efficient. For instance the polarimetric images are represented on the Poincare sphere which maps in a natural way to the qubit Bloch sphere. Thus, polarimetric images will not be any more processed as “signal” but directly as a physical signature. Further will be discussed the advantages of quantum annealing (D-Wave) for solving local optimization for non-convex problems. Also, the potential and advantage of the recent TensorFlow Quantum and the implementation of parametrized quantum circuits (PQC). The presentation will address the entire image analyis cycle encompassing the particular features from data acquisition, understanding and modelling of the image sensor, followed by information extraction. The quantum ML techniques are practically implemented using the open access to various quantum computers, as D-Wave, IBM, or Google. Hybrid methods will be discussed for satellite observations, i.e. managing the I/O of the data and maximally use the resources of quantum computers and quantum algorithms.