Quantum-Assisted Optimization Guided by Machine-Learned Risk Maps for Aerial Surveillance Routing
quant-ph, math.OC
Submitted: 2026-10-02
Updated: 2026-10-02
Comments: 26 pages, 5 figures, 3 tables
Code: https://github.com/QUASAR-UdS/TOP
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Quantum Optimization for Maximum Independent Set Using Rydberg Atom Arrays
- Industry applications of neutral-atom quantum computing solving independent set problems
- Leveraging Analog Neutral Atom Quantum Computers for Diversified Pricing in Hybrid Column Generation Frameworks
- The State of Aerial Surveillance: A Survey
- A Dynamic Unmanned Aerial Vehicle Routing Framework for Urban Traffic Monitoring
- Solving Drone Routing Problems with Quantum Computing: A Hybrid Approach Combining Quantum Annealing and Gate-Based Paradigms
- XGBoost meets INLA: a two-stage spatio-temporal forecasting of wildfires in Portugal
- Local Rydberg blockade regimes for disk graph embedding and quantum optimization
- Efficient protocol for solving combinatorial graph problems on neutral-atom quantum processors
- Quantum Optimization on Rydberg Atom Arrays with Arbitrary Connectivity: Gadgets Limitations and a Heuristic Approach
- Efficient Emulation of Neutral Atom Quantum Hardware
- A Quantum Approximate Optimization Algorithm
- Financial Risk Management on a Neutral Atom Quantum Processor
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