Hyperspectral image segmentation with a machine learning model trained using quantum annealer

Since the energy consumption becomes a major problem in the development and implementation of artificial intelligence systems there exists a need to investigate the ways to reduce use of the resources by these systems. In this work we study how application of quantum annealers could lead to reduction of energy cost in training models aiming at pixel-level segmentation of hyperspec-tral images. Following the results of QBM4EO team, we propose a classical machine learning model, partially trained using quantum annealer, for hyperspectral image segmentation. We show that the model trained using quantum annealer is better or at least comparable with models trained using alternative algorithms, according to the preselected, common metrics. While direct energy use comparison does not make sense at the current stage of quantum computing technology development, we believe that our work proves that quantum annealing should be considered as a tool for training at least some machine learning models. Keywords: RBM, QML, Hyperspectral imaging, image segmentation 1 Introduction The rapid growth of artificial intelligence, especially in the field of generative models [18] and transformer architecture in 2017 [41] has lead to a major proliferation of large deep learning models. It is becoming a major concern that economic opportunities that are believed to be existing coming from the explosion of large models, lead to major energy consumption related to training and using these models. In order to mitigate this problem it is important to search for alternative methods of models training. In this work we employ an old idea and implement it on a new hardware device -- 1 arXiv:2503.01400v1