Can quantum computing speed equitable disaster recovery?
Q-RESTORE: Quantum-Driven Framework for Resilient and Equitable Transportation Network Restoration
This paper introduces Q-RESTORE, a framework using quantum computing (specifically D-Wave's hybrid quantum solver) to restore transportation networks after disasters, prioritizing equitable resource allocation for low-income communities. It compares this approach to a traditional genetic algorithm (GA).
While not directly employing LLMs, the research emphasizes optimization and resource allocation in a multi-agent environment (road users and a public authority) with budget constraints and equity considerations. This core concept of optimizing agent behavior under constraints relates to LLM-based multi-agent system development, where LLMs can act as agents, requiring optimization strategies for their actions and resource usage, potentially incorporating fairness and equity metrics. The quantum solver's demonstrated speed and efficiency suggest similar computational approaches could be valuable in complex, resource-intensive LLM-based multi-agent scenarios, though further research is needed to explore this connection.