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001     1020574
005     20250204113747.0
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037 _ _ |a FZJ-2024-00269
082 _ _ |a 520
100 1 _ |a Pasetto, Edoardo
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245 _ _ |a Kernel Approximation on a Quantum Annealer for Remote Sensing Regression Tasks
260 _ _ |a New York, NY
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520 _ _ |a 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 (RS) community. This paper proposes an (AQKS) kernel approximation algorithm with parallel quantum annealing on the D-Wave Advantage quantum annealer. The proposed implementation is applied to (SVR) and (GPR) algorithms. To evaluate its performance, a regression problem related to estimating chlorophyll concentration in water is considered. The proposed algorithm was tested on two real-world datasets and its results were compared with those obtained by a classical implementation of kernel-based algorithms and a (RKS) implementation. On average, the parallel (AQKS) achieved comparable results to the benchmark methods, indicating its potential for future applications.
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700 1 _ |a Michielsen, Kristel
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700 1 _ |a Cavallaro, Gabriele
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