pith:INXPENX7
Low Latency GNN Accelerator for Quantum Error Correction
An FPGA accelerator for a graph neural network decoder performs quantum error correction in under one microsecond with lower error rates than prior methods for surface codes up to distance 7.
arxiv:2603.22149 v3 · 2026-03-23 · quant-ph · cs.AR
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\pithnumber{INXPENX76OS7TTTJAEA3DQ5NHU}
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Record completeness
Claims
the FPGA-based decoder achieving a latency smaller than 1μs, with a lower error rate compared to the state-of-the-art for code distance up to d=7
That hardware-aware optimizations applied to the GNN decoder preserve its accuracy sufficiently to outperform prior decoders while meeting the strict 1us timing constraint imposed by superconducting qubit coherence times.
An FPGA-accelerated GNN decoder for surface-code quantum error correction delivers sub-1us latency and lower error rates than state-of-the-art approaches for code distances up to 7.
Cited by
Receipt and verification
| First computed | 2026-07-09T01:19:53.229653Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
436ef236fff3a5f9ce690101b1c3ad3d11d43a2e3b2cb4d54a0c8ac10a083684
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/INXPENX76OS7TTTJAEA3DQ5NHU \
| 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: 436ef236fff3a5f9ce690101b1c3ad3d11d43a2e3b2cb4d54a0c8ac10a083684
Canonical record JSON
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"license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
"primary_cat": "quant-ph",
"submitted_at": "2026-03-23T16:14:52Z",
"title_canon_sha256": "dc3f6af4bae434728ff432853bf22b1a5fa7ef03fbaed32f943acc47306c1e70"
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