{"paper":{"title":"AI-Enabled Decoding of Qubit Loss for Quantum Error-Correcting Codes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A graph neural network decoder corrects both Pauli errors and qubit loss locations from syndrome histories with higher accuracy than matching algorithms.","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Hui Zhai, Jiale Dai, Linghui Chen, Tao Zhang, Xiaotian Nie, Yuqing Wang, Zhongyi Ni","submitted_at":"2026-04-15T17:59:35Z","abstract_excerpt":"Qubit loss is a major source of error in quantum computation, as it invalidates the algebraic structure of the standard stabilizer formalism for quantum error-correcting codes. On the one hand, it complicates decoding; on the other hand, it introduces stochastic flicker patterns in stabilizers as a hallmark of qubit loss. Here, we develop an artificial-intelligence-enabled decoder based on a spatiotemporal Graph Neural Network (STGNN) architecture to extract spatial and temporal correlations from syndrome histories. Our decoder performs a dual-head task, simultaneously correcting standard Paul"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"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.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"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.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A graph neural network decoder corrects both Pauli errors and qubit loss locations from syndrome histories with higher accuracy than matching algorithms.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"f2cc0aaf5670bf0383c2222c4c60dd6662a60f62168aaecd36c1153fdd2e585c"},"source":{"id":"2604.14269","kind":"arxiv","version":2},"verdict":{"id":"63908b13-9984-4099-ae8b-3dc282d2c20c","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T13:26:32.461473Z","strongest_claim":"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.","one_line_summary":"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.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"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.","pith_extraction_headline":"A graph neural network decoder corrects both Pauli errors and qubit loss locations from syndrome histories with higher accuracy than matching algorithms."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.14269/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}