aiqbench analysis
This research demonstrates a practical hybrid approach where classical machine learning generates risk maps for drone surveillance, while analog quantum annealing optimizes the resulting routing paths to maximize wildfire detection probability. By framing the problem as a constrained Team Orienteering Problem, the study validates the utility of neutral atom quantum computers in solving complex combinatorial optimization tasks that are difficult for classical systems alone. The work provides early empirical evidence on how scaling quantum resources can enhance heuristic performance in real-world logistics and safety applications.
Quantum-Assisted Optimization Guided by Machine-Learned Risk Maps for Aerial Surveillance Routing
arXiv:2610.03890v1 Announce Type: new Abstract: Analog quantum computing with neutral atom computers is rapidly evolving into a promising way to perform combinatorial optimization tasks at scale. As technology matures, exploring real use cases leveraging such unique information processors becomes warranted; one example is logistics management where vehicles are assigned routes to perform a given task. Autonomous aerial vehicles (drones) are increasingly used for surveillance over a given territory. In this paper, we propose an innovative framework integrating classical machine learning forecasts and quantum annealing to find the best combination of tours for a given surveillance criterion. The risk maps produced by machine learning are used to find possible surveillance tours, while a quantum-assisted heuristic chooses the best combination, producing a full surveillance plan. By modelling our decision problem for drone paths as a Team Orienteering Problem (TOP) with additional constraints, we construct a new heuristic using quantum annealing to find good solutions. We apply this framework to wildfire prevention, where finding tours most likely to detect a starting fire has clear benefits. We study the effect of larger quantum resources on heuristic performance and compare those results to the use of classical resources.
Abstract:Analog quantum computing with neutral atom computers is rapidly evolving into a promising way to perform combinatorial optimization tasks at scale. As technology matures, exploring real use cases leveraging such unique information processors becomes warranted; one example is logistics management where vehicles are assigned routes to perform a given task. Autonomous aerial vehicles (drones) are increasingly used for surveillance over a given territory. In this paper, we propose an innovative framework integrating classical machine learning forecasts and quantum annealing to find the best combination of tours for a given surveillance criterion. The risk maps produced by machine learning are used to find possible surveillance tours, while a quantum-assisted heuristic chooses the best combination, producing a full surveillance plan. By modelling our decision problem for drone paths as a Team Orienteering Problem (TOP) with additional constraints, we construct a new heuristic using quantum annealing to find good solutions. We apply this framework to wildfire prevention, where finding tours most likely to detect a starting fire has clear benefits. We study the effect of larger quantum resources on heuristic performance and compare those results to the use of classical resources.
Quantum Physics (quant-ph); Optimization and Control (math.OC)
arXiv:2610.03890 [quant-ph]
(or arXiv:2610.03890v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2610.03890 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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