Adiabatic Quantum Kitchen Sinks with Parallel Annealing for Remote Sensing Regression Problems

Kernel methods are class of Machine Learning (ML) models that have been widely employed in the literature for Earth Observation (EO) applications. The increasing development of quantum computing hardware motivates further research to improve the capabilities and the performances of data analysis algorithms. In this manuscript an implementation of Adiabatic Quantum Kitchen Sinks (AQKS) kernel estimation algorithm integrated with parallel quantum annealing is presented. Such combination with the concept of parallel quantum annealing allows for the solving of multiple problem instances in the same annealing cycle, thus reducing the number of rquired calls to the quantum annealing solver. The proposed workflow is then implemented using a D-Wave Advantage system and tested on a regression problem on a real Remote Sensing (RS) dataset. The obtained results are then analyzed and compared with those obtained by a classical kernel approximation algorithm based on Random Fourier Features.