{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:E6ZVHQAYVBJMHLHZ3UKLINRFNE","short_pith_number":"pith:E6ZVHQAY","schema_version":"1.0","canonical_sha256":"27b353c018a852c3acf9dd14b43625691cb218fc343b764ed086072801a3aa19","source":{"kind":"arxiv","id":"2503.21374","version":1},"attestation_state":"computed","paper":{"title":"Generative Decoding for Quantum Error-correcting Codes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Dongyang Feng, Feng Pan, Hanyan Cao, Pan Zhang, Yijia Wang","submitted_at":"2025-03-27T11:08:03Z","abstract_excerpt":"Efficient and accurate decoding of quantum error-correcting codes is essential for fault-tolerant quantum computation, however, it is challenging due to the degeneracy of errors, the complex code topology, and the large space for logical operators in high-rate codes. In this work, we propose a decoding algorithm utilizing generative modeling in machine learning. We employ autoregressive neural networks to learn the joint probability of logical operators and syndromes in an unsupervised manner, eliminating the need for labeled training data. The learned model can approximately perform maximum l"},"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":"2503.21374","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2025-03-27T11:08:03Z","cross_cats_sorted":[],"title_canon_sha256":"fd00726842aef4e298c6867884d36e6009b8e12e8c548472dcc6a79eae7afb5d","abstract_canon_sha256":"de30c5dcf1fde60ba02a6fb7c6be5affae8a8578e51141a66acecf0bd98ed9da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:40:24.489514Z","signature_b64":"e3Xff0YLl+R56Seg8sTsYubwhGW4SDxtq5ffmyu7g916jdGO87OQvR5P1V1i37cGs9xppTUCwQn33RzBEg1kCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"27b353c018a852c3acf9dd14b43625691cb218fc343b764ed086072801a3aa19","last_reissued_at":"2026-07-05T10:40:24.489113Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:40:24.489113Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generative Decoding for Quantum Error-correcting Codes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Dongyang Feng, Feng Pan, Hanyan Cao, Pan Zhang, Yijia Wang","submitted_at":"2025-03-27T11:08:03Z","abstract_excerpt":"Efficient and accurate decoding of quantum error-correcting codes is essential for fault-tolerant quantum computation, however, it is challenging due to the degeneracy of errors, the complex code topology, and the large space for logical operators in high-rate codes. In this work, we propose a decoding algorithm utilizing generative modeling in machine learning. We employ autoregressive neural networks to learn the joint probability of logical operators and syndromes in an unsupervised manner, eliminating the need for labeled training data. The learned model can approximately perform maximum l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.21374","kind":"arxiv","version":1},"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/2503.21374/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":"2503.21374","created_at":"2026-07-05T10:40:24.489169+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.21374v1","created_at":"2026-07-05T10:40:24.489169+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.21374","created_at":"2026-07-05T10:40:24.489169+00:00"},{"alias_kind":"pith_short_12","alias_value":"E6ZVHQAYVBJM","created_at":"2026-07-05T10:40:24.489169+00:00"},{"alias_kind":"pith_short_16","alias_value":"E6ZVHQAYVBJMHLHZ","created_at":"2026-07-05T10:40:24.489169+00:00"},{"alias_kind":"pith_short_8","alias_value":"E6ZVHQAY","created_at":"2026-07-05T10:40:24.489169+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.17230","citing_title":"Maximum Likelihood Decoding of Quantum Error Correction Codes","ref_index":93,"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":23,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14269","citing_title":"AI-Enabled Decoding of Qubit Loss for Quantum Error-Correcting Codes","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E6ZVHQAYVBJMHLHZ3UKLINRFNE","json":"https://pith.science/pith/E6ZVHQAYVBJMHLHZ3UKLINRFNE.json","graph_json":"https://pith.science/api/pith-number/E6ZVHQAYVBJMHLHZ3UKLINRFNE/graph.json","events_json":"https://pith.science/api/pith-number/E6ZVHQAYVBJMHLHZ3UKLINRFNE/events.json","paper":"https://pith.science/paper/E6ZVHQAY"},"agent_actions":{"view_html":"https://pith.science/pith/E6ZVHQAYVBJMHLHZ3UKLINRFNE","download_json":"https://pith.science/pith/E6ZVHQAYVBJMHLHZ3UKLINRFNE.json","view_paper":"https://pith.science/paper/E6ZVHQAY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.21374&json=true","fetch_graph":"https://pith.science/api/pith-number/E6ZVHQAYVBJMHLHZ3UKLINRFNE/graph.json","fetch_events":"https://pith.science/api/pith-number/E6ZVHQAYVBJMHLHZ3UKLINRFNE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E6ZVHQAYVBJMHLHZ3UKLINRFNE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E6ZVHQAYVBJMHLHZ3UKLINRFNE/action/storage_attestation","attest_author":"https://pith.science/pith/E6ZVHQAYVBJMHLHZ3UKLINRFNE/action/author_attestation","sign_citation":"https://pith.science/pith/E6ZVHQAYVBJMHLHZ3UKLINRFNE/action/citation_signature","submit_replication":"https://pith.science/pith/E6ZVHQAYVBJMHLHZ3UKLINRFNE/action/replication_record"}},"created_at":"2026-07-05T10:40:24.489169+00:00","updated_at":"2026-07-05T10:40:24.489169+00:00"}