pith:SFHE5D7H
QUACOD: Quantum Optimization via Coordinate Descent for Scalable Drone Scheduling
QUACOD decomposes drone scheduling into quantum-solvable subproblems to scale five times larger than direct methods on limited qubits.
arxiv:2605.14001 v1 · 2026-05-13 · quant-ph
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Claims
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.
That decomposing the original high-complexity drone scheduling problem via coordinate descent into subproblems solved independently on quantum hardware yields solutions that are competitive with or better than solving the full problem directly.
QUACOD decomposes drone scheduling into quantum-solvable subproblems via coordinate descent, outperforming prior quantum methods in completion time while scaling to 5x more drones and 35x more routes.
References
Receipt and verification
| First computed | 2026-05-17T23:39:13.155839Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
914e4e8fe7f07e9ec4320d3ece128813f23dd374e129d5440fe29370265eb4ed
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/SFHE5D7H6B7J5RBSBU7M4EUICP \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 914e4e8fe7f07e9ec4320d3ece128813f23dd374e129d5440fe29370265eb4ed
Canonical record JSON
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