用量子退火器辅助训练高光谱图像分割模型，降低能耗潜力。
Hyperspectral image segmentation with a machine learning model trained using quantum annealer
- 部分利用量子退火器预训练经典模型，减少能量消耗。
- 在常见指标上表现优于或至少相当传统训练方法。
- 为量子计算用于特定机器学习训练提供新思路，适合关注能效的团队。
机器学习模型训练消耗大量能源，已成为人工智能系统发展与应用的主要瓶颈。本文研究量子退火器在高光谱图像像素级分割模型训练中的应用，旨在降低能耗。基于QBM4EO团队的研究成果，提出一种经典机器学习模型，其部分参数通过量子退火器进行预训练。实验表明，该模型在预设的通用评估指标上表现优于或至少相当于传统算法训练的模型。尽管当前量子计算技术尚无法直接比较能耗，但本工作证明了量子退火应被视为训练特定机器学习模型的潜在工具。
原文摘要 · Abstract (English)
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.
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