Abstract:
In response to numerous important circumstances, such as disease outbreaks and catastrophe management, the efficient deployment of resources for spatial isolation and quarantine measures are critical.The goal is to strategically position barricades and checkpoints to ensure optimal spatial separation, minimizing disease spread while maximizing resource utilization. Our research focuses on two approaches: classical computing techniques such as the Kruskal Algorithm and quantum computing techniques such as Quantum Annealing, Hybrid Annealing, and Simulated Annealing. We compare these strategies in terms of computational efficiency, specifically in terms of execution time. Quantum Annealing, which takes advantage of D-Wave’s quantum annealer capabilities, emerges as the most promising approach, consistently offering optimal solutions to the MST problem. However, more research is needed to understand the nature of Hybrid and Simulated Annealing in overcoming this essential challenge. Our findings indicates that Quantum Annealing has significant computing speed advantages, highlighting its potential as a powerful tool for optimizing resource allocation in geographic isolation and quarantine settings. Furthermore, the research illuminates the adaptability and performance of Hybrid and Simulated Annealing in addressing complicated combinatorial optimization problems, with potential for parameter customization and investigation. The findings not only sheds light on efficient quarantine techniques, but also offer up new paths for research at the interface of quantum computing and operational science.
