In Human-Centered Computing, the emphasis is on designing computing technologies that prioritize human needs, behaviors, and experiences. Multi-Agent Systems with their inherent collaborative capabilities seamlessly fit into this paradigm. Efficient teamwork among the distributed autonomous agents comprising a Multi-Agent System is essential for applications that directly impact human-centric domains, such as disaster management and smart healthcare. Forming optimal coalitions between multiple agents is called the Coalition Formation Problem and it is an NP-hard problem. Metaheuristic optimization techniques are popularly used to tackle NP-hard problems as they offer practical solutions that balance computational complexity with the need for effective problem-solving in various domains. Of late, quantum-inspired metaheuristics have gained traction due to their strong local and global search capability, superior output compared to the non-quantum versions, and effectiveness even with a small population. This article succinctly outlines various quantum-inspired metaheuristic optimization methods that have been proposed for coalition formation in multi-agent systems. It examines their key features and strengths, identifies existing gaps, while providing a pointer for further research.