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Kernel Approximation on a Quantum Annealer for Remote Sensing Regression Tasks
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  • Edoardo Pasetto ,
  • Morris Riedel ,
  • Kristel Michielsen ,
  • Gabriele Cavallaro
Edoardo Pasetto
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Morris Riedel
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Kristel Michielsen
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Gabriele Cavallaro
Forschungszentrum J├╝lich

Corresponding Author:[email protected]

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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.