Kernel Approximation on a Quantum Annealer for Remote Sensing Regression Tasks

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.