Quantum-Assisted Route Optimization for UAV Offshore Geological Exploration

Abstract

Marine geological exploration faces increasing operational and environmental challenges, ranging from ecological constraints and high costs to the scarcity of high-resolution geospatial data. This work introduces a quantum-enhanced simulation framework that combines Quadratic Unconstrained Binary Optimization (QUBO) and the Quantum Approximate Optimization Algorithm (QAOA) to optimize route planning in the early stages of geological reconnaissance in offshore basins of Colombia. The 1 km² study area is discretized into a 10×10 grid of 100 m × 100 m cells, each assigned a geostructural priority score. The resulting QUBO formulation integrates spatial connectivity, movement rules, and geological relevance into a binary decision structure, which is then solved using hybrid quantum-classical optimization routines. The findings show that QAOA-based solutions generate coherent and efficient exploration trajectories, capable of prioritizing high-value geological targets without compromising spatial continuity or compliance with energy constraints. Overall, the proposed framework outlines a promising pathway toward low-impact and cost-efficient marine exploration strategies, scalable to broader maritime contexts. Furthermore, it is compatible with future extensions involving AI-assisted geological classification and real-time adaptive route planning through.