Exploiting the Quantum Advantage for Satellite Image Processing: Quantum
Resource Estimation
Abstract
We first review the current state of the art of quantum computing for
Earth observation (EO) 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. Secondly,
we assess some parameterized quantum circuit 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 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 Satellite Images
(HSIs); HSIs are a unique 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.