pith:BMCXOTJK
Worst-Case Sample Complexity Bounds for Distributed Inner Product Estimation with Local Randomized Measurements
Local Clifford measurements achieve O(√(4.5^n)) worst-case sample complexity for distributed inner product estimation on n-qubit states.
arxiv:2605.14256 v1 · 2026-05-14 · quant-ph
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Claims
For this kernel under local Clifford sampling, we prove a sharp fourth-moment bound using the single-qubit Clifford commutant. This yields worst-case sample complexity O(√(4.5^n)), attained by identical pure product stabilizer states.
The reduction of the minimax kernel optimization to Hamming-distance kernels, together with the claim that unbiasedness fixes a unique kernel within this class.
The work proves a worst-case sample complexity of O(sqrt(4.5^n)) for distributed inner product estimation with local Clifford sampling on n-qubit states, with a conjectured O(sqrt(3.6^n)) for Haar sampling.
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| First computed | 2026-05-17T23:39:10.526009Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
0b05774d2a77c60d2201c46d342fa73f368cc0ceaeb4c6fca7e285a4c99aefef
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/BMCXOTJKO7DA2IQBYRWTIL5HH4 \
| 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())"
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Canonical record JSON
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