# Quantum Computing for Climate Resilience and Sustainability Challenges

arXiv: https://arxiv.org/abs/2407.16296 · HTML: https://mlsanity.com/paper/2407.16296/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 5 of 5)

- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [The complexity of quantum support vector machines](https://mlsanity.com/paper/2203.00031/) (2022)
- [Quantum-Enhanced Support Vector Machine for Large-Scale Stellar Classification with GPU Acceleration](https://mlsanity.com/paper/2311.12328/) (2023)
- [Quantum Annealing Continuous Optimisation in Renewable Energy](https://mlsanity.com/paper/2105.11322/) (2021)
- [HamilToniQ: An Open-Source Benchmark Toolkit for Quantum Computers](https://mlsanity.com/paper/2404.13971/) (2024)

## Built on by (most cited first, 7 of 7)

- [Quantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecasting](https://mlsanity.com/paper/2412.08851/) (2024)
- [Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2412.08845/) (2024)
- [Toward Large-Scale Distributed Quantum Long Short-Term Memory with Modular Quantum Computers](https://mlsanity.com/paper/2503.14088/) (2025)
- [CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data](https://mlsanity.com/paper/2409.08584/) (2024)
- [Quantum-Trained Convolutional Neural Network for Deepfake Audio Detection](https://mlsanity.com/paper/2410.09250/) (2024)
- [Quantum Kernel-Based Long Short-term Memory](https://mlsanity.com/paper/2411.13225/) (2024)
- [Consensus-based Distributed Quantum Kernel Learning for Speech Recognition](https://mlsanity.com/paper/2409.05770/) (2024)

## Abstract (arXiv)

The escalating impacts of climate change and the increasing demand for sustainable development and natural resource management necessitate innovative technological solutions. Quantum computing (QC) has emerged as a promising tool with the potential to revolutionize these critical areas. This review explores the application of quantum machine learning and optimization techniques for climate change prediction and enhancing sustainable development. Traditional computational methods often fall short in handling the scale and complexity of climate models and natural resource management. Quantum advancements, however, offer significant improvements in computational efficiency and problem-solving capabilities. By synthesizing the latest research and developments, this paper highlights how QC and quantum machine learning can optimize multi-infrastructure systems towards climate neutrality. The paper also evaluates the performance of current quantum algorithms and hardware in practical applications and presents realistic cases, i.e., waste-to-energy in anaerobic digestion, disaster prevention in flooding prediction, and new material development for carbon capture. The integration of these quantum technologies promises to drive significant advancements in achieving climate resilience and sustainable development.