Solving Drone Routing Problems with Quantum Computing: A Hybrid Approach Combining Quantum Annealing and Gate-Based Paradigms

2025-01-30

AI Summary

This work addresses the drone routing problem under realistic constraints—including asymmetric costs, prohibited paths, and mobile charging stations. To tackle this computationally challenging task, we propose Q4DR, a hybrid quantum computing framework operating in two stages: (1) quantum approximate optimization algorithm (QAOA) on a gate-model quantum processor for regional clustering; and (2) fine-grained path optimization via D-Wave quantum annealing. To our knowledge, this is the first approach synergistically integrating gate-model and quantum-annealing paradigms for drone path planning with strong real-world constraints. Implemented using Eclipse Qrisp, Q4DR is evaluated on three progressively scaled real-world scenarios. Results demonstrate significant improvements in both computational efficiency and solution quality—particularly in feasibility and constraint satisfaction—for large-scale constrained routing problems. This work establishes a novel paradigm for leveraging quantum computing to advance practical logistics optimization.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Combinatorial Optimization
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper presents a novel hybrid approach to solving real-world drone routing problems by leveraging the capabilities of quantum computing. The proposed method, coined Quantum for Drone Routing (Q4DR), integrates the two most prominent paradigms in the field: quantum gate-based computing, through the Eclipse Qrisp programming language; and quantum annealers, by means of D-Wave System's devices. The algorithm is divided into two different phases: an initial clustering phase executed using a Quantum Approximate Optimization Algorithm (QAOA), and a routing phase employing quantum annealers. The efficacy of Q4DR is demonstrated through three use cases of increasing complexity, each incorporating real-world constraints such as asymmetric costs, forbidden paths, and itinerant charging points. This research contributes to the growing body of work in quantum optimization, showcasing the practical applications of quantum computing in logistics and route planning.

Problem

• Drone Routing
• Logistics Optimization
• Path Planning

Innovation

• Quantum Computing
• Drone Path Planning
• Quantum Approximate Optimization Algorithm (QAOA)