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