Utilizing quantum algorithms to achieve carbon neutrality in urban areas: A systematic review

In the face of climate change, urban areas, as major sources of greenhouse gas (GHG) emissions, are pivotal in mitigation efforts. Cities worldwide are striving to achieve carbon neutrality, aligning with the Sustainable Development Goals (SDGs). Quantum computing (QC), with its ability to leverage qubits and surpass the limitations of classical computing, presents a promising avenue for optimizing processes in energy, transportation, and climate change mitigation. This study conducts a systematic literature review following Kitchenham's framework, analyzing 96 out of 3397 articles from four major academic databases. The review focuses on the implementation of quantum algorithms, including pure quantum algorithms (PQA), hybrid quantum-classical algorithms (HQC), and quantum-inspired classical algorithms (QIC), with 40 distinct methods such as Quantum Annealing (QA), Quantum-inspired Particle Swarm Optimization (QiPSO), and Quantum Approximate Optimization Algorithm (QAOA). The analysis identifies key application domains: algorithmic tasks, types of implications, GHG emission sectors, and quantum methods, highlighting allocation and routing tasks as dominant themes. Notably, the transportation and energy sectors, along with the QA method, feature prominently. Furthermore, a detailed nexus analysis reveals significant interconnections between algorithmic tasks, quantum methods, and their practical implications, particularly in energy efficiency and resource allocation. The findings underscore the growing role of quantum algorithms in reducing urban GHG emissions and advancing toward carbon neutrality, showcasing QC's potential in supporting sustainable cities and effective climate action. (c) 2017 Elsevier Inc. All rights reserved.