Quantum-Assisted Optimization Guided by Machine-Learned Risk Maps for Aerial Surveillance Routing
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.
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BibTeX
@misc{lauwerier2026quantumassisted,
title = {{Quantum-Assisted Optimization Guided by Machine-Learned Risk Maps for Aerial Surveillance Routing}},
author = {Dorian Lauwerier and Alexis Vieloszynski and Yves Bérubé-Lauzière and Victor Drouin-Touchette},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.03890},
eprint = {2610.03890},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.03890},
}
RIS
TY - UNPB
TI - Quantum-Assisted Optimization Guided by Machine-Learned Risk Maps for Aerial Surveillance Routing
AU - Dorian Lauwerier
AU - Alexis Vieloszynski
AU - Yves Bérubé-Lauzière
AU - Victor Drouin-Touchette
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2610.03890
UR - https://arxiv.org/abs/2610.03890
AB - 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.
ER -