{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:D2IM322QX4L4WIUVWOOYSRGPVR","short_pith_number":"pith:D2IM322Q","schema_version":"1.0","canonical_sha256":"1e90cdeb50bf17cb2295b39d8944cfac4108b612a3e16229ac34cde0569457e0","source":{"kind":"arxiv","id":"2208.05758","version":2},"attestation_state":"computed","paper":{"title":"NEO-QEC: Neural Network Enhanced Online Superconducting Decoder for Surface Codes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"quant-ph","authors_text":"Masaaki Kondo, Masamitsu Tanaka, Yasunari Suzuki, Yosuke Ueno, Yutaka Tabuchi","submitted_at":"2022-08-11T11:37:09Z","abstract_excerpt":"Quantum error correction (QEC) is essential for quantum computing to mitigate the effect of errors on qubits, and surface code (SC) is one of the most promising QEC methods. Decoding SCs is the most computational expensive task in the control device of quantum computers (QCs), and many works focus on accurate decoding algorithms for SCs, including ones with neural networks (NNs). Practical QCs also require low-latency decoding because slow decoding leads to the accumulation of errors on qubits, resulting in logical failures. For QCs with superconducting qubits, a practical decoder must be very"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2208.05758","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2022-08-11T11:37:09Z","cross_cats_sorted":["cs.AR"],"title_canon_sha256":"6e431d2dd7e9bd1a00ee0135bdf64a6530631fa9d1c4b50f2cc8d689c7c5c283","abstract_canon_sha256":"a3484e053b97fd67ace41ea7e6ea5e5417fd75c4d5939da375725c21f3ad0669"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:53:43.834959Z","signature_b64":"YNs5thr8YRo4lERPEVM34nfReKCQ+eDS+bBUhEInrxhJfEz1xhQDwYGV2n4Dsf8huFDpBlafKM0DUxvAa19vCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e90cdeb50bf17cb2295b39d8944cfac4108b612a3e16229ac34cde0569457e0","last_reissued_at":"2026-07-05T04:53:43.834506Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:53:43.834506Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NEO-QEC: Neural Network Enhanced Online Superconducting Decoder for Surface Codes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"quant-ph","authors_text":"Masaaki Kondo, Masamitsu Tanaka, Yasunari Suzuki, Yosuke Ueno, Yutaka Tabuchi","submitted_at":"2022-08-11T11:37:09Z","abstract_excerpt":"Quantum error correction (QEC) is essential for quantum computing to mitigate the effect of errors on qubits, and surface code (SC) is one of the most promising QEC methods. Decoding SCs is the most computational expensive task in the control device of quantum computers (QCs), and many works focus on accurate decoding algorithms for SCs, including ones with neural networks (NNs). Practical QCs also require low-latency decoding because slow decoding leads to the accumulation of errors on qubits, resulting in logical failures. For QCs with superconducting qubits, a practical decoder must be very"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.05758","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2208.05758/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2208.05758","created_at":"2026-07-05T04:53:43.834565+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.05758v2","created_at":"2026-07-05T04:53:43.834565+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.05758","created_at":"2026-07-05T04:53:43.834565+00:00"},{"alias_kind":"pith_short_12","alias_value":"D2IM322QX4L4","created_at":"2026-07-05T04:53:43.834565+00:00"},{"alias_kind":"pith_short_16","alias_value":"D2IM322QX4L4WIUV","created_at":"2026-07-05T04:53:43.834565+00:00"},{"alias_kind":"pith_short_8","alias_value":"D2IM322Q","created_at":"2026-07-05T04:53:43.834565+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2406.17995","citing_title":"Managing Classical Processing Requirements for Quantum Error Correction","ref_index":61,"is_internal_anchor":false},{"citing_arxiv_id":"2506.16113","citing_title":"Fully convolutional 3D neural network decoders for surface codes with syndrome circuit noise","ref_index":45,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D2IM322QX4L4WIUVWOOYSRGPVR","json":"https://pith.science/pith/D2IM322QX4L4WIUVWOOYSRGPVR.json","graph_json":"https://pith.science/api/pith-number/D2IM322QX4L4WIUVWOOYSRGPVR/graph.json","events_json":"https://pith.science/api/pith-number/D2IM322QX4L4WIUVWOOYSRGPVR/events.json","paper":"https://pith.science/paper/D2IM322Q"},"agent_actions":{"view_html":"https://pith.science/pith/D2IM322QX4L4WIUVWOOYSRGPVR","download_json":"https://pith.science/pith/D2IM322QX4L4WIUVWOOYSRGPVR.json","view_paper":"https://pith.science/paper/D2IM322Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.05758&json=true","fetch_graph":"https://pith.science/api/pith-number/D2IM322QX4L4WIUVWOOYSRGPVR/graph.json","fetch_events":"https://pith.science/api/pith-number/D2IM322QX4L4WIUVWOOYSRGPVR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D2IM322QX4L4WIUVWOOYSRGPVR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D2IM322QX4L4WIUVWOOYSRGPVR/action/storage_attestation","attest_author":"https://pith.science/pith/D2IM322QX4L4WIUVWOOYSRGPVR/action/author_attestation","sign_citation":"https://pith.science/pith/D2IM322QX4L4WIUVWOOYSRGPVR/action/citation_signature","submit_replication":"https://pith.science/pith/D2IM322QX4L4WIUVWOOYSRGPVR/action/replication_record"}},"created_at":"2026-07-05T04:53:43.834565+00:00","updated_at":"2026-07-05T04:53:43.834565+00:00"}