Exploiting the Quantum Advantage for Satellite Image Processing: Quantum Resource Estimation

We first review the current state of the art of quantum computing for Earth observation and satellite images. There are the persisting challenges of profiting from quantum advantage, and finding the optimal sharing between high-performance computing (HPC) and quantum computing (QC), i.e. the HPC+QC paradigm, for computational EO problems and Artificial Intelligence (AI) approaches. Secondly, we assess some quantum models transpiled into a Clifford+T universal gate set, where the Clifford+T quantum gate set sheds light on the quantum resources required for deploying quantum models either on an HPC system or several QCs. If the Clifford+T quantum gate set cannot be simulated efficiently on an HPC system then we can apply a quantum computer and its computational power over conventional computers. Our resulting quantum resource estimation demonstrates that Quantum Machine Learning (QML) models, which do not comprise a large number of T-gates, can be deployed on an HPC system during the training and validation process; otherwise, we can execute them on several QCs. Namely, QML models having a sufficient number of T-gates provide the quantum advantage if and only if they generalize on unseen data points better than their classical counterparts deployed on the HPC system, and they break the symmetry in their weights at each learning iteration like in conventional deep neural networks. As an initial innovation, we estimate the quantum resources required for some QML models. Secondly, we define the optimal sharing between an HPC+QC system for executing QML models for hyperspectral images (HSIs); HSIs are a specific dataset compared to multispectral images to be deployed on quantum computers due to the limited number of their input qubits, and the commonly used small number of labeled benchmark HSIs.