Quantum SVR for Chlorophyll Concentration Estimation in Water with
Remote Sensing
Abstract
The increasing availability of quantum computers motivates researching
their potential capabilities in enhancing the performance of data
analysis algorithms. Similarly as in other research communities, also in
Remote Sensing (RS) it is not yet defined how its applications can
benefit from the usage of quantum computing. This paper proposes a
formulation of the Support Vector Regression (SVR) algorithm that can be
executed by D-Wave quantum computers. Specifically, the SVR is mapped to
a Quadratic Unconstrained Binary Optimization (QUBO) optimization
problem that is solved with Quantum Annealing (QA). The algorithm is
tested on two different types of computing environments offered by
D-Wave: The Advantage system, which directly embeds the problem into the
Quantum Processing Unit (QPU), and a Hybrid solver that employs both
classical and quantum computing resources. For the evaluation, we
considered a biophysical variable estimation problem with RS data. The
experimental results show that the proposed quantum SVR implementation
can achieve comparable or in some cases better results than the
classical implementation. This work is one of the first attempts to
provide insight into how QA could be exploited and integrated in future
RS workflows based on Machine Learning algorithms.