{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NJEMTZRNSLUKZXZVXLP644UF5C","short_pith_number":"pith:NJEMTZRN","schema_version":"1.0","canonical_sha256":"6a48c9e62d92e8acdf35badfee7285e8a3b8ba730c566bee85ca23e09475c6bc","source":{"kind":"arxiv","id":"2506.00321","version":1},"attestation_state":"computed","paper":{"title":"QTP-Net: A Quantum Text Pre-training Network for Natural Language Processing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Ren-Xin Zhao","submitted_at":"2025-05-31T00:17:35Z","abstract_excerpt":"Natural Language Processing (NLP) faces challenges in the ability to quickly model polysemous words. The Grover's Algorithm (GA) is expected to solve this problem but lacks adaptability. To address the above dilemma, a Quantum Text Pre-training Network (QTP-Net) is proposed to improve the performance of NLP tasks. First, a Quantum Enhanced Pre-training Feature Embedding (QEPFE) is developed to encode multiple meanings of words into quantum superposition states and exploit adaptive GA to fast capture rich text features. Subsequently, the QEPFE is combined with the Enhanced Representation throug"},"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":"2506.00321","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2025-05-31T00:17:35Z","cross_cats_sorted":[],"title_canon_sha256":"f34c378edbe08f019ab4450a475502136321cb7b7d8a120fb1b15d94381d98fb","abstract_canon_sha256":"1eb6319b626506dec86641a83fb20624b43fb5a674cc30262521bbbd25a6fa14"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:32.489459Z","signature_b64":"HVsv/dHq47HKwC4NR5/L4Ql7EhwAFDWuW+X8jgHrCh4b7H6uYwLe7fUF5NGzCAoNgrNZzurXTXdhpkSCaIv+Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6a48c9e62d92e8acdf35badfee7285e8a3b8ba730c566bee85ca23e09475c6bc","last_reissued_at":"2026-07-05T11:13:32.489023Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:32.489023Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"QTP-Net: A Quantum Text Pre-training Network for Natural Language Processing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Ren-Xin Zhao","submitted_at":"2025-05-31T00:17:35Z","abstract_excerpt":"Natural Language Processing (NLP) faces challenges in the ability to quickly model polysemous words. The Grover's Algorithm (GA) is expected to solve this problem but lacks adaptability. To address the above dilemma, a Quantum Text Pre-training Network (QTP-Net) is proposed to improve the performance of NLP tasks. First, a Quantum Enhanced Pre-training Feature Embedding (QEPFE) is developed to encode multiple meanings of words into quantum superposition states and exploit adaptive GA to fast capture rich text features. Subsequently, the QEPFE is combined with the Enhanced Representation throug"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.00321","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/2506.00321/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":"2506.00321","created_at":"2026-07-05T11:13:32.489083+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.00321v1","created_at":"2026-07-05T11:13:32.489083+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.00321","created_at":"2026-07-05T11:13:32.489083+00:00"},{"alias_kind":"pith_short_12","alias_value":"NJEMTZRNSLUK","created_at":"2026-07-05T11:13:32.489083+00:00"},{"alias_kind":"pith_short_16","alias_value":"NJEMTZRNSLUKZXZV","created_at":"2026-07-05T11:13:32.489083+00:00"},{"alias_kind":"pith_short_8","alias_value":"NJEMTZRN","created_at":"2026-07-05T11:13:32.489083+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/NJEMTZRNSLUKZXZVXLP644UF5C","json":"https://pith.science/pith/NJEMTZRNSLUKZXZVXLP644UF5C.json","graph_json":"https://pith.science/api/pith-number/NJEMTZRNSLUKZXZVXLP644UF5C/graph.json","events_json":"https://pith.science/api/pith-number/NJEMTZRNSLUKZXZVXLP644UF5C/events.json","paper":"https://pith.science/paper/NJEMTZRN"},"agent_actions":{"view_html":"https://pith.science/pith/NJEMTZRNSLUKZXZVXLP644UF5C","download_json":"https://pith.science/pith/NJEMTZRNSLUKZXZVXLP644UF5C.json","view_paper":"https://pith.science/paper/NJEMTZRN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.00321&json=true","fetch_graph":"https://pith.science/api/pith-number/NJEMTZRNSLUKZXZVXLP644UF5C/graph.json","fetch_events":"https://pith.science/api/pith-number/NJEMTZRNSLUKZXZVXLP644UF5C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NJEMTZRNSLUKZXZVXLP644UF5C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NJEMTZRNSLUKZXZVXLP644UF5C/action/storage_attestation","attest_author":"https://pith.science/pith/NJEMTZRNSLUKZXZVXLP644UF5C/action/author_attestation","sign_citation":"https://pith.science/pith/NJEMTZRNSLUKZXZVXLP644UF5C/action/citation_signature","submit_replication":"https://pith.science/pith/NJEMTZRNSLUKZXZVXLP644UF5C/action/replication_record"}},"created_at":"2026-07-05T11:13:32.489083+00:00","updated_at":"2026-07-05T11:13:32.489083+00:00"}