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

arXiv: https://arxiv.org/abs/2503.01400 · HTML: https://mlsanity.com/paper/2503.01400/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Training and Classification using a Restricted Boltzmann Machine on the D-Wave 2000Q](https://mlsanity.com/paper/2005.03247/) (2020)
- [Snapshot Multispectral Imaging Using a Diffractive Optical Network](https://mlsanity.com/paper/2212.05217/) (2022)
- [Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale](https://mlsanity.com/paper/1605.05797/) (2016)
- [Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time Capability](https://mlsanity.com/paper/2207.12939/) (2022)
- [A Dataset for Evaluating Blood Detection in Hyperspectral Images](https://mlsanity.com/paper/2008.10254/) (2020)
- [EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing Imagery](https://mlsanity.com/paper/1804.00292/) (2018)

## Abstract (arXiv)

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.