Quantum computing in addressing greenhouse gas emissions: A systematic literature review

Abstract

The greenhouse gas (GHG) emissions issue that is directly related to the 13th Sustainable Development Goals; Climate Action has gained attention on a global scale, prompting the utilization of all available technological advancements, including quantum computing. This systematic literature review, employing Kitchenham’s method, explores the realm of quantum computing and its application to the pressing issue of GHG emissions. Through a meticulous analysis of scholarly articles, we identify key trends, influential authors, core sources, and relevant affiliations within this research domain. Notably, our findings underscore a robust connection between quantum computing studies and the fields of machine learning and optimization, where various optimization tasks attempt to minimize GHG emissions, predominantly in the Energy and Logistics problem domain using Quantum-inspired Evolutionary Algorithm, Quantum-inspired Swarm Optimization, or Quantum Annealing. An insightful map reveals the emergence of diverse quantum computing implementations for varied tasks, across various domains, providing nuanced perspectives and identifying potential research directions, particularly in optimization and prediction tasks. This study offers a foundational understanding of trends, challenges, and opportunities associated with quantum computing implementation in addressing GHG emissions, contributing to the ongoing establishment of sustainable technology.