Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment

TL;DR

This paper reviews the current state and potential of quantum computing in satellite image processing, assessing quantum advantage, resource requirements, and optimal hybrid HPC-quantum systems for Earth observation data.

Contribution

It introduces quantum resource estimation for QML models on satellite data and defines optimal HPC+QC sharing strategies for hyperspectral image analysis.

Findings

Quantum models can outperform classical ones if they generalize better on unseen data.

Quantum resource estimation indicates the number of T-gates needed for advantage.

Hyperspectral satellite images are less challenging for quantum deployment due to limited qubits.

Abstract

This article examines the current status of quantum computing in Earth observation (EO) and satellite imagery. We analyze the potential limitations and applications of quantum learning models when dealing with satellite data, considering the persistent challenges of profiting from quantum advantage and finding the optimal sharing between high-performance computing (HPC) and quantum computing (QC). We then assess some parameterized quantum circuit models transpiled into a Clifford+T universal gate set. The T-gates shed light on the quantum resources required to deploy quantum models, either on an HPC system or several QC systems. In particular, if the T-gates cannot be simulated efficiently on an HPC system, we can apply a quantum computer and its computational power over conventional techniques. Our quantum resource estimation showed that quantum machine learning (QML) models, with 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. We also estimated the quantum resources required for some QML models as an initial innovation. Lastly, we defined the optimal sharing between an HPC+QC system for executing QML models for hyperspectral satellite images. These are a unique dataset compared to other satellite images since they have a limited number of input qubits and a small number of labeled benchmark images, making them less challenging to deploy on quantum computers.