The increased development of quantum computing hardware in recent years has led to increased interest in its application to various areas. Finding effective ways to apply this technology to real-world use-cases is a current area of research in the Remote Sensing (RS) community. This paper proposes an Adiabatic Quantum Kitchen Sinks (AQKS) kernel approximation algorithm with parallel quantum annealing on the D-Wave Advantage quantum annealer. The proposed implementation is applied to Support Vector Regression (SVR) and Gaussian Process Regression (GPR) algorithms. To evaluate its performance, a regression problem related to estimating chlorophyll concentra-tion in water is considered. The proposed algorithm was tested on two real-world datasets and its results were compared with those obtained from a classical implementation of kernel-based algorithms and a Random Kitchen Sinks (RKS) implementation. On average, the parallel AQKS achieved comparable results to the benchmark methods, indicating its potential for future applications.

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.