aiqbench analysis
This research introduces QUACOD, a novel algorithmic framework designed to tackle drone scheduling optimization within the constraints of current NISQ-era quantum hardware. By decomposing complex logistics problems into manageable subproblems via coordinate descent, the method significantly improves scalability and route efficiency compared to existing state-of-the-art approaches. The work highlights the viability of using limited qubit counts for practical applications in delivery networks without requiring fault-tolerant machines.
QUACOD: Quantum Optimization via Coordinate Descent for Scalable Drone Scheduling
arXiv:2605.14001v2 Announce Type: replace Abstract: Quantum computing has demonstrated its potential to solve various optimization problems, including drone scheduling, which is important not only for drone delivery but also for logistics in general. However, one of the main obstacles is that practical drone scheduling settings typically require quantum resources that current hardware cannot provide. Therefore, in this work, we introduce a new Quantum Optimization via Coordinate Descent (QUACOD) approach to address this problem under the constraint of a limited number of available qubits. By leveraging coordinate descent, QUACOD decomposes the original high-complexity problem into multiple subproblems, which are then solved using quantum optimization. In our experiments, QUACOD outperforms the state-of-the-art (SOTA) quantum-based drone scheduling method not only in optimized drone completion times but also in scalability, handling up to 5 times more drones and 35 times more routes. In addition, QUACOD demonstrates that hardware-efficient circuits are effective for optimization problems. Together, these contributions advance quantum computing toward practical applications in the noisy intermediate-scale quantum (NISQ) era.
Abstract:Quantum computing has demonstrated its potential to solve various optimization problems, including drone scheduling, which is important not only for drone delivery but also for logistics in general. However, one of the main obstacles is that practical drone scheduling settings typically require quantum resources that current hardware cannot provide. Therefore, in this work, we introduce a new Quantum Optimization via Coordinate Descent (QUACOD) approach to address this problem under the constraint of a limited number of available qubits. By leveraging coordinate descent, QUACOD decomposes the original high-complexity problem into multiple subproblems, which are then solved using quantum optimization. In our experiments, QUACOD outperforms the state-of-the-art (SOTA) quantum-based drone scheduling method not only in optimized drone completion times but also in scalability, handling up to 5 times more drones and 35 times more routes. In addition, QUACOD demonstrates that hardware-efficient circuits are effective for optimization problems. Together, these contributions advance quantum computing toward practical applications in the noisy intermediate-scale quantum (NISQ) era.
arXiv:2605.14001 [quant-ph]
(or arXiv:2605.14001v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2605.14001 Focus to learn more arXiv-issued DOI via DataCite
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