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arXiv:2508.21361 · IEEE International Conference on Quantum Artificial Intelligence (QAI)

QUAV: Quantum-Assisted Path Planning and Optimization for UAV Navigation with Obstacle Avoidance

Nouhaila Innan, Muhammad Kashif, Alberto Marchisio, Yung-Sze Gan, Frédéric Barbaresco, Muhammad Shafique

Published Nov 2, 2025• 3 citations
arXivHTMLPDF

Abstract

The growing demand for drone navigation in urban and restricted airspaces requires real-time path planning that is both safe and scalable. Classical methods often struggle with the computational load of high-dimensional optimization under dynamic constraints like obstacle avoidance and no-fly zones. This work introduces QUAV, a quantum-assisted UAV path planning framework based on the Quantum Approximate Optimization Algorithm (QAOA), to the best of our knowledge, this is one of the first applications of QAOA for drone trajectory optimization. QUAV models pathfinding as a quantum optimization problem, allowing efficient exploration of multiple paths while incorporating obstacle constraints and geospatial accuracy through UTM coordinate transformation. A theoretical analysis shows that QUAV achieves linear scaling in circuit depth relative to the number of edges, under fixed optimization settings. Extensive simulations and a realhardware implementation on IBM's ibm_kyiv backend validate its performance and robustness under noise. Despite hardware constraints, results demonstrate that QUAV generates feasible, efficient trajectories, highlighting the promise of quantum approaches for future drone navigation systems.

DOI
10.1109/qai63978.2025.00040

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The growing demand for drone navigation in urban and restricted airspaces requires real-time path planning that is both safe and scalable. Classical methods often struggle with the computational load of high-dimensional optimization under dynamic constraints like obstacle avoidance and no-fly zones. This work introduces QUAV, a quantum-assisted UAV path planning framework based on the Quantum Approximate Optimization Algorithm (QAOA), to the best of our knowledge, this is one of the first applications of QAOA for drone trajectory optimization. QUAV models pathfinding as a quantum optimization problem, allowing efficient exploration of multiple paths while incorporating obstacle constraints and geospatial accuracy through UTM coordinate transformation. A theoretical analysis shows that QUAV achieves linear scaling in circuit depth relative to the number of edges, under fixed optimization settings. Extensive simulations and a realhardware implementation on IBM's ibm_kyiv backend validate its performance and robustness under noise. Despite hardware constraints, results demonstrate that QUAV generates feasible, efficient trajectories, highlighting the promise of quantum approaches for future drone navigation systems.
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Mission planning for multiple unmanned aerial vehicles (UAVs) is a complex problem that is expected to be solved by quantum computing. With the increasing application of UAVs, the demand for efficient conflict management strategies to ensure airspace safety continues to increase. In the era of noisy intermediate-scale quantum (NISQ) devices, variational quantum algorithms (VQA) for optimizing parameterized quantum circuits with the help of classical optimizers are currently one of the most promising strategies to gain quantum advantage. In this paper, we propose a mathematical model for the UAV collision avoidance problem that maps the collision avoidance problem to a quadratic unconstrained binary optimization (QUBO) problem. The problem is formulated as an Ising Hamiltonian, then the ground state is solved using two kinds of VQAs: the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA). We select conditional value-at-risk (CVaR) to further promote the performance of our model. Four examples are given to validate that with our method the probability of obtaining a feasible solution can exceed 90% based on appropriate parameters, and our method can enhance the efficiency of a UAVs' collision avoidance model.
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Offshore geological exploration increasingly confronts operational and environmental challenges, including ecological constraints, elevated costs, and limited availability of high-resolution geospatial data. This study proposes a quantum-enhanced simulation framework that integrates Quadratic Unconstrained Binary Optimization (QUBO) with the Quantum Approximate Optimization Algorithm (QAOA) to optimize route planning for early-stage geological reconnaissance in the Guajira Offshore basin, Colombia. The study domain—a 1 km² marine area—is discretized into a 10×10 grid of 100 m × 100 m cells, each assigned a geostructural priority score. The resulting QUBO formulation encodes spatial connectivity, movement rules, and geological relevance into a binary decision structure, which is then solved through hybrid quantum–classical optimization routines. Results indicate that QAOA-based solutions produce coherent and efficient exploration routes that prioritize high-value geological targets while respecting spatial continuity and energy limitations. The proposed framework offers a promising pathway toward low-impact, cost-effective offshore exploration strategies and is scalable to broader maritime contexts. Additionally, it is compatible with future extensions involving AI-driven geological classification and real-time, adaptive route planning using multi-agent systems.