Hybrid Quantum-Classical Multi-Agent Pathfinding

TL;DR

This paper introduces the first hybrid quantum-classical algorithms for multi-agent pathfinding, leveraging quantum computing to solve conflict graphs iteratively, demonstrating superior performance over existing methods on benchmarks.

Contribution

It presents novel hybrid quantum-classical MAPF algorithms based on branch-and-cut-and-price, integrating quantum solutions for conflict graph problems, advancing the state-of-the-art.

Findings

Our approach outperforms previous QUBO formulations.

Experimental results on quantum hardware validate the method.

The hybrid algorithm is effective on benchmark datasets.

Abstract

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.