Quantum SVR for Chlorophyll Concentration Estimation in Water With Remote Sensing

IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5

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) 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 (ML) algorithms.

Authors 5

1. Edoardo Pasetto Aachen

   RWTH Aachen University · Jülich Supercomputing Centre

   Affiliation as printed

   Jülich Supercomputing Centre, Jülich, Germany

   RWTH Aachen University, Aachen, Germany

2. Morris Riedel

   University of Iceland · Jülich Supercomputing Centre

   Affiliation as printed

   Jülich Supercomputing Centre, Jülich, Germany

   University of Iceland, Reykjavik, Iceland

3. Farid Melgani

   University of Trento

   Affiliation as printed

   Department of Information Engineering and Computer Science, University of Trento, Trento, Italy

   Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, Trento, Italy

4. Kristel F. L. Michielsen Aachen

   RWTH Aachen University · Jülich Supercomputing Centre

   Affiliation as printed

   Jülich Supercomputing Centre, Jülich, Germany

   RWTH Aachen University, Aachen, Germany

5. Gabriele Cavallaro

   University of Iceland · Jülich Supercomputing Centre

   Affiliation as printed

   Jülich Supercomputing Centre, Jülich, Germany

   University of Iceland, Reykjavik, Iceland