Quantum

Quantum-Assisted Optimization Guided by Machine-Learned Risk Maps for Aerial Surveillance Routing

Read full article at source ↗

Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 06/10/2026, 04:13:25 UTC.

Briefings

This paper proposes a framework integrating classical machine learning risk maps with quantum annealing to optimize drone surveillance routes for wildfire prevention. The study models the problem as a Team Orienteering Problem (TOP) and tests the impact of varying quantum resource sizes on heuristic performance, comparing results against classical computational approaches.

Key points

• The study models drone path selection as a Team Orienteering Problem (TOP) with additional constraints to identify optimal surveillance tours.
• Machine learning generates risk maps that guide the heuristic, while quantum annealing selects the best combination of tours for the given surveillance criterion.

Context and limitations

The research focuses on wildfire prevention scenarios and compares quantum versus classical resource performance; specific results for other surveillance criteria are not provided.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 06/10/2026, 05:14:05 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

View archived briefing on IPFS
bafybeigbtz5sttfygfbhh7jg3afpeudzj7dnxaapyrvc4buifxayyczmpa