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pith:SFHE5D7H

pith:2026:SFHE5D7H6B7J5RBSBU7M4EUICP
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QUACOD: Quantum Optimization via Coordinate Descent for Scalable Drone Scheduling

Hoang-Quan Nguyen, Ilya Safro, Khoa Luu, Samee U. Khan, Van-Quang-Huy Nguyen

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

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

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

38 extracted · 38 resolved · 1 Pith anchors

[1] Drones in last-mile delivery: A systematic literature review from a lo- gistics management perspective, 2025
[2] Drone routing for drone-based delivery systems: A review of trajectory planning, charging, and security, 2023
[3] The traveling salesman problem: a computational study, 2011
[4] Exact methods for the traveling salesman problem with drone, 2021
[5] A review of last- mile delivery optimization: Strategies, technologies, drone integration, and future trends, 2025
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

arxiv: 2605.14001 · arxiv_version: 2605.14001v1 · doi: 10.48550/arxiv.2605.14001 · pith_short_12: SFHE5D7H6B7J · pith_short_16: SFHE5D7H6B7J5RBS · pith_short_8: SFHE5D7H
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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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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "quant-ph",
    "submitted_at": "2026-05-13T18:08:52Z",
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