Hybrid Quantum-Classical Multi-Agent Pathfinding

2025-01-24

AI Summary

Large-scale Multi-Agent Path Finding (MAPF) suffers from poor scalability on classical hardware, while existing quantum approaches lack practical applicability. Method: This paper proposes the first quantum-classical hybrid optimal algorithm for MAPF, built upon the Branch-Cut-and-Reward framework. It innovatively integrates quantum computation into the core optimal solving pipeline: conflicts are modeled via a conflict graph; QUBO subproblems are iteratively generated; and critical subinstances are solved on real quantum hardware (e.g., IBM Quantum). Contribution/Results: The method significantly improves scalability and efficiency, outperforming state-of-the-art QUBO encodings and classical optimal MAPF solvers on standard benchmarks and physical quantum devices. It provides the first empirical validation of the quantum-classical hybrid paradigm for real-world multi-agent planning—demonstrating its effectiveness, robustness, and engineering feasibility.

Technology Category

Multiagent Systems: Multiagent PlanningMachine Learning: Quantum Machine LearningSearch and Optimization: Mixed Discrete/Continuous Search

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingResponsible Web: Human-perceived consequences of algorithmic deployment on the webEconomics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

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 algorithm which is based on branch-and-cut-and-prize. 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 formulations and baseline MAPF solvers.

Problem

• Multi-Agent Path Finding
• Quantum Computing Limitations
• Collision Avoidance

Innovation

• Quantum Computing
• Multi-Agent Path Finding (MAPF)
• Hybrid Algorithm