{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:SSTZMW6X2JCVTH7RATHIRKZAKT","short_pith_number":"pith:SSTZMW6X","schema_version":"1.0","canonical_sha256":"94a7965bd7d245599ff104ce88ab2054e83a4f96693554377694cb2242ae976b","source":{"kind":"arxiv","id":"2607.25865","version":1},"attestation_state":"computed","paper":{"title":"OmniQEC: discovering practical quantum error-correcting codes by an AI scientist","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.MA"],"primary_cat":"quant-ph","authors_text":"Ge Yan, Jianping Wang, Min-Hsiu Hsieh, Pengyue Ma, Pingchuan Ma, Qixin Zhang, Shanchuan Li, Yuxuan Du","submitted_at":"2026-07-28T15:31:26Z","abstract_excerpt":"Quantum error correction (QEC) is indispensable for scalable fault-tolerant quantum computing. However, discovering QEC codes that remain effective is challenging, as logical performance depends on the interplay between code structure, hardware, syndrome extraction, and decoding, which often impose competing requirements. Here we introduce OmniQEC, an efficient AI scientist for discovering QEC codes suited to deployment on modern quantum processors. OmniQEC formulates QEC design as an iterative discovery process in which an orchestrator, implemented by advanced large language models (LLMs), co"},"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":"2607.25865","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2026-07-28T15:31:26Z","cross_cats_sorted":["cs.AI","cs.MA"],"title_canon_sha256":"6f961235573d8d9205c4f8788b6a2aae7996671dcc0695c64ed24d244ba15267","abstract_canon_sha256":"e4302ff3eb64244206700f20cacad4f7977c3ac51d26c6acb371688bd25b7ba9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-29T01:26:07.566115Z","signature_b64":"WPjzc+Lv7y69m6Poq00zn1/YU40tMBrbI6bW3NQ/+gLLRk69zWa9ezxJMgdUokypn5ZSA6GxPTXN76MGcyR6DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"94a7965bd7d245599ff104ce88ab2054e83a4f96693554377694cb2242ae976b","last_reissued_at":"2026-07-29T01:26:07.565211Z","signature_status":"signed_v1","first_computed_at":"2026-07-29T01:26:07.565211Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OmniQEC: discovering practical quantum error-correcting codes by an AI scientist","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.MA"],"primary_cat":"quant-ph","authors_text":"Ge Yan, Jianping Wang, Min-Hsiu Hsieh, Pengyue Ma, Pingchuan Ma, Qixin Zhang, Shanchuan Li, Yuxuan Du","submitted_at":"2026-07-28T15:31:26Z","abstract_excerpt":"Quantum error correction (QEC) is indispensable for scalable fault-tolerant quantum computing. However, discovering QEC codes that remain effective is challenging, as logical performance depends on the interplay between code structure, hardware, syndrome extraction, and decoding, which often impose competing requirements. Here we introduce OmniQEC, an efficient AI scientist for discovering QEC codes suited to deployment on modern quantum processors. OmniQEC formulates QEC design as an iterative discovery process in which an orchestrator, implemented by advanced large language models (LLMs), co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.25865","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/2607.25865/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":"2607.25865","created_at":"2026-07-29T01:26:07.565670+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.25865v1","created_at":"2026-07-29T01:26:07.565670+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.25865","created_at":"2026-07-29T01:26:07.565670+00:00"},{"alias_kind":"pith_short_12","alias_value":"SSTZMW6X2JCV","created_at":"2026-07-29T01:26:07.565670+00:00"},{"alias_kind":"pith_short_16","alias_value":"SSTZMW6X2JCVTH7R","created_at":"2026-07-29T01:26:07.565670+00:00"},{"alias_kind":"pith_short_8","alias_value":"SSTZMW6X","created_at":"2026-07-29T01:26:07.565670+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SSTZMW6X2JCVTH7RATHIRKZAKT","json":"https://pith.science/pith/SSTZMW6X2JCVTH7RATHIRKZAKT.json","graph_json":"https://pith.science/api/pith-number/SSTZMW6X2JCVTH7RATHIRKZAKT/graph.json","events_json":"https://pith.science/api/pith-number/SSTZMW6X2JCVTH7RATHIRKZAKT/events.json","paper":"https://pith.science/paper/SSTZMW6X"},"agent_actions":{"view_html":"https://pith.science/pith/SSTZMW6X2JCVTH7RATHIRKZAKT","download_json":"https://pith.science/pith/SSTZMW6X2JCVTH7RATHIRKZAKT.json","view_paper":"https://pith.science/paper/SSTZMW6X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.25865&json=true","fetch_graph":"https://pith.science/api/pith-number/SSTZMW6X2JCVTH7RATHIRKZAKT/graph.json","fetch_events":"https://pith.science/api/pith-number/SSTZMW6X2JCVTH7RATHIRKZAKT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SSTZMW6X2JCVTH7RATHIRKZAKT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SSTZMW6X2JCVTH7RATHIRKZAKT/action/storage_attestation","attest_author":"https://pith.science/pith/SSTZMW6X2JCVTH7RATHIRKZAKT/action/author_attestation","sign_citation":"https://pith.science/pith/SSTZMW6X2JCVTH7RATHIRKZAKT/action/citation_signature","submit_replication":"https://pith.science/pith/SSTZMW6X2JCVTH7RATHIRKZAKT/action/replication_record"}},"created_at":"2026-07-29T01:26:07.565670+00:00","updated_at":"2026-07-29T01:26:07.565670+00:00"}