Hyperspectral image segmentation with a machine learning model trained using quantum annealer
This work addresses energy efficiency in AI training for hyperspectral image analysis, but it is incremental as it builds on prior quantum annealing research without direct energy comparisons.
The authors tackled the problem of high energy consumption in training machine learning models by using a quantum annealer to partially train a model for hyperspectral image segmentation, showing it performs comparably or better than alternative algorithms based on common metrics.
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.