# Quantum Machine Learning in Climate Change and Sustainability: a Review

arXiv: https://arxiv.org/abs/2310.09162 · HTML: https://mlsanity.com/paper/2310.09162/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 13 Oct 2023.

## Builds on (most cited first, 3 of 3)

- [Quantum machine learning for image classification](https://mlsanity.com/paper/2304.09224/) (2023)
- [On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification](https://mlsanity.com/paper/2109.09484/) (2021)
- [Quantum Artificial Intelligence for the Science of Climate Change](https://mlsanity.com/paper/2108.10855/) (2021)

## Built on by (most cited first, 1 of 1)

- [On the impact of key design aspects in simulated Hybrid Quantum Neural Networks for Earth Observation](https://mlsanity.com/paper/2410.08677/) (2024)

## Abstract (arXiv)

Climate change and its impact on global sustainability are critical challenges, demanding innovative solutions that combine cutting-edge technologies and scientific insights. Quantum machine learning (QML) has emerged as a promising paradigm that harnesses the power of quantum computing to address complex problems in various domains including climate change and sustainability. In this work, we survey existing literature that applies quantum machine learning to solve climate change and sustainability-related problems. We review promising QML methodologies that have the potential to accelerate decarbonization including energy systems, climate data forecasting, climate monitoring, and hazardous events predictions. We discuss the challenges and current limitations of quantum machine learning approaches and provide an overview of potential opportunities and future work to leverage QML-based methods in the important area of climate change research.