标题： 使用量子退火器训练的机器学习模型进行高光谱图像分割 显示英文标题

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

摘要： 机器学习模型的训练消耗大量能源。由于能源消耗已成为人工智能系统开发和实施中的主要问题，因此需要研究减少这些系统资源使用的方法。在这项工作中，我们研究了量子退火器的应用如何能够降低训练用于高光谱图像像素级分割模型的能耗。根据QBM4EO团队的结果，我们提出了一种部分通过量子退火器训练的经典机器学习模型，用于高光谱图像分割。我们表明，根据预先选定的通用指标，使用量子退火器训练的模型优于或至少与使用替代算法训练的模型相当。虽然在当前量子计算技术发展阶段直接比较能耗没有意义，但我们认为我们的工作证明了量子退火应该被视为训练至少某些机器学习模型的工具。 显示英文摘要

摘要： Training of machine learning models consumes large amounts of energy. 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 hyperspectral 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.