pith:Z5HIGTJ6
Hybrid Quantum-Classical Optimization Workflows for the Shipment Selection Problem
A hybrid quantum-classical workflow for the shipment selection problem improves deliveries by up to 12 percent and reduces drive distance by up to 6 percent on tested instances when quantum assignments serve as warm starts.
arxiv:2604.11758 v2 · 2026-04-13 · quant-ph
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Claims
When the quantum assignment is passed to the classical solver as a warm start, the resulting hybrid workflow achieves improvements of up to 12% in SD and a reduction of up to 6% in total drive distance per shipment for specific instances, while total operational cost remains effectively unchanged.
That the assignments produced by Iterative-QAOA on the tested instances supply a meaningfully superior warm start compared with classical heuristics or random initialization for the same problem sizes and data.
Hybrid Iterative-QAOA warm starts improve shipment delivery by up to 12% and cut drive distance by 6% on real logistics data when fed to a classical solver.
Receipt and verification
| First computed | 2026-06-09T01:04:42.757144Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
cf4e834d3e65dcb872459258b567b0020ffe8214daf248f0a5085ec82796ba9b
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/Z5HIGTJ6MXOLQ4SFSJMLKZ5QAI \
| 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: cf4e834d3e65dcb872459258b567b0020ffe8214daf248f0a5085ec82796ba9b
Canonical record JSON
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