Pith Number
pith:DLHYH2JS
pith:2022:DLHYH2JSJOH6T6A7M33H5WAE5Z
not attested
not anchored
not stored
refs pending
Quantum Approximate Optimization Algorithm Parameter Prediction Using a Convolutional Neural Network
arxiv:2211.09513 v3 · 2022-11-17 · quant-ph
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{DLHYH2JSJOH6T6A7M33H5WAE5Z}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
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claim
4
Citations
5
Replications
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Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Receipt and verification
| First computed | 2026-07-05T08:10:11.953884Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
1acf83e9324b8fe9f81f66f67ed804ee6f411419c8f709b2e23c7e0693c45fd3
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/DLHYH2JSJOH6T6A7M33H5WAE5Z \
| 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: 1acf83e9324b8fe9f81f66f67ed804ee6f411419c8f709b2e23c7e0693c45fd3
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "c7a790a67277fe4e1ae8ab221891dd193ba9b8be54c5e45d030801ee175f16f4",
"cross_cats_sorted": [],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "quant-ph",
"submitted_at": "2022-11-17T13:20:58Z",
"title_canon_sha256": "964406b18040b4bdac0d58caff0e09f751108c56884892627fafe3f1af86235f"
},
"schema_version": "1.0",
"source": {
"id": "2211.09513",
"kind": "arxiv",
"version": 3
}
}