Hybrid Quantum-Classical Multi-Agent Pathfinding
This work addresses the problem of efficient pathfinding for multiple agents, such as autonomous vehicles, by leveraging quantum computing, though it is incremental as it builds on existing quantum-classical methods.
The paper tackled the computationally challenging Multi-Agent Path Finding (MAPF) problem by developing the first optimal hybrid quantum-classical algorithms based on branch-and-cut-and-price, integrating quantum computing via QUBO problems. Experiments showed that this approach outperforms previous QUBO formulations and state-of-the-art MAPF solvers.
Multi-Agent Path Finding (MAPF) focuses on determining conflict-free paths for multiple agents navigating through a shared space to reach specified goal locations. This problem becomes computationally challenging, particularly when handling large numbers of agents, as frequently encountered in practical applications like coordinating autonomous vehicles. Quantum Computing (QC) is a promising candidate in overcoming such limits. However, current quantum hardware is still in its infancy and thus limited in terms of computing power and error robustness. In this work, we present the first optimal hybrid quantum-classical MAPF algorithms which are based on branch-andcut-and-price. QC is integrated by iteratively solving QUBO problems, based on conflict graphs. Experiments on actual quantum hardware and results on benchmark data suggest that our approach dominates previous QUBO formulationsand state-of-the-art MAPF solvers.