As drone traffic and air taxi services expand globally, regulators face increasing pressure to manage complex airspace safely, while operators aim to optimise energy-efficient routes. Traditional computing may not scale to meet future path planning demands, making quantum computing a promising alternative. However, the variety of qubit modalities across quantum processing units (QPUs) raises questions about which platforms are most suitable for such applications. This paper presents a hybrid quantum-classical drone path planning approach that accounts for urban obstacles and its implementation on two QPUs: D-Wave’s quantum annealer and Pasqal’s neutral atom processor. We compare solution quality, time-to-solution, and hardware-specific trade-offs against a classical solver, providing insights into the scalability, embedding challenges, and the practical feasibility of different quantum computing platforms for urban air mobility.