pith:OOM3DE7J
AI-Enabled Decoding of Qubit Loss for Quantum Error-Correcting Codes
A graph neural network decoder corrects both Pauli errors and qubit loss locations from syndrome histories with higher accuracy than matching algorithms.
arxiv:2604.14269 v2 · 2026-04-15 · quant-ph
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Claims
Our decoder achieves significantly higher logical accuracy than both the traditional minimum-weight perfect matching (MWPM) algorithm and even delayed-erasure MWPM decoders that use qubit loss information from the final round as input. Our decoder can also identify more than 90% of loss locations after accumulating stabilizer measurements over the subsequent ten rounds.
That the spatiotemporal correlations present in simulated syndrome histories are sufficient for the STGNN to reliably separate qubit loss events from Pauli errors and that the learned model will generalize to real hardware noise without retraining.
An STGNN decoder outperforms standard and delayed-erasure MWPM algorithms in logical accuracy while recovering more than 90% of qubit loss locations after ten measurement rounds.
Receipt and verification
| First computed | 2026-05-26T02:04:10.882969Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
7399b193e9757360c9e871ab42d9e4951a8991c6396d239d97328b1f8b6ca786
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/OOM3DE7JOVZWBSPIOGVUFWPESU \
| 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: 7399b193e9757360c9e871ab42d9e4951a8991c6396d239d97328b1f8b6ca786
Canonical record JSON
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