{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2QQJ4VS63M5CJFTVVW4YPWDRDY","short_pith_number":"pith:2QQJ4VS6","schema_version":"1.0","canonical_sha256":"d4209e565edb3a249675adb987d8711e3ecdbd32786ced5120ba1687b22e1717","source":{"kind":"arxiv","id":"2401.07777","version":1},"attestation_state":"computed","paper":{"title":"Quantum Transfer Learning for Acceptability Judgements","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.comp-ph","quant-ph"],"primary_cat":"cs.CL","authors_text":"Aniello Minutolo, Giuseppe Buonaiuto, Giuseppe De Pietro, Massimo Esposito, Raffaele Guarasci","submitted_at":"2024-01-15T15:40:16Z","abstract_excerpt":"Hybrid quantum-classical classifiers promise to positively impact critical aspects of natural language processing tasks, particularly classification-related ones. Among the possibilities currently investigated, quantum transfer learning, i.e., using a quantum circuit for fine-tuning pre-trained classical models for a specific task, is attracting significant attention as a potential platform for proving quantum advantage.\n  This work shows potential advantages, both in terms of performance and expressiveness, of quantum transfer learning algorithms trained on embedding vectors extracted from a "},"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":"2401.07777","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-15T15:40:16Z","cross_cats_sorted":["physics.comp-ph","quant-ph"],"title_canon_sha256":"185a3217e0a3b8d169a57393d5259bc5ff960a75643d5c6adb77216341f7d4be","abstract_canon_sha256":"a1b868911c0a46b9c270991ab2beb0867ee7655c88cb3a0004e096dcb1bc5056"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:34:02.793459Z","signature_b64":"e352qZB+5kCdRVvoSDCcxqJflxsGj+LobAEg9uf3f15PLj5+jf7kGRpY+B4I6/nRFgp15IPuApRfz7rP4n/YBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d4209e565edb3a249675adb987d8711e3ecdbd32786ced5120ba1687b22e1717","last_reissued_at":"2026-07-05T07:34:02.793035Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:34:02.793035Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum Transfer Learning for Acceptability Judgements","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.comp-ph","quant-ph"],"primary_cat":"cs.CL","authors_text":"Aniello Minutolo, Giuseppe Buonaiuto, Giuseppe De Pietro, Massimo Esposito, Raffaele Guarasci","submitted_at":"2024-01-15T15:40:16Z","abstract_excerpt":"Hybrid quantum-classical classifiers promise to positively impact critical aspects of natural language processing tasks, particularly classification-related ones. Among the possibilities currently investigated, quantum transfer learning, i.e., using a quantum circuit for fine-tuning pre-trained classical models for a specific task, is attracting significant attention as a potential platform for proving quantum advantage.\n  This work shows potential advantages, both in terms of performance and expressiveness, of quantum transfer learning algorithms trained on embedding vectors extracted from a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.07777","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/2401.07777/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":"2401.07777","created_at":"2026-07-05T07:34:02.793084+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.07777v1","created_at":"2026-07-05T07:34:02.793084+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.07777","created_at":"2026-07-05T07:34:02.793084+00:00"},{"alias_kind":"pith_short_12","alias_value":"2QQJ4VS63M5C","created_at":"2026-07-05T07:34:02.793084+00:00"},{"alias_kind":"pith_short_16","alias_value":"2QQJ4VS63M5CJFTV","created_at":"2026-07-05T07:34:02.793084+00:00"},{"alias_kind":"pith_short_8","alias_value":"2QQJ4VS6","created_at":"2026-07-05T07:34:02.793084+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/2QQJ4VS63M5CJFTVVW4YPWDRDY","json":"https://pith.science/pith/2QQJ4VS63M5CJFTVVW4YPWDRDY.json","graph_json":"https://pith.science/api/pith-number/2QQJ4VS63M5CJFTVVW4YPWDRDY/graph.json","events_json":"https://pith.science/api/pith-number/2QQJ4VS63M5CJFTVVW4YPWDRDY/events.json","paper":"https://pith.science/paper/2QQJ4VS6"},"agent_actions":{"view_html":"https://pith.science/pith/2QQJ4VS63M5CJFTVVW4YPWDRDY","download_json":"https://pith.science/pith/2QQJ4VS63M5CJFTVVW4YPWDRDY.json","view_paper":"https://pith.science/paper/2QQJ4VS6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.07777&json=true","fetch_graph":"https://pith.science/api/pith-number/2QQJ4VS63M5CJFTVVW4YPWDRDY/graph.json","fetch_events":"https://pith.science/api/pith-number/2QQJ4VS63M5CJFTVVW4YPWDRDY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2QQJ4VS63M5CJFTVVW4YPWDRDY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2QQJ4VS63M5CJFTVVW4YPWDRDY/action/storage_attestation","attest_author":"https://pith.science/pith/2QQJ4VS63M5CJFTVVW4YPWDRDY/action/author_attestation","sign_citation":"https://pith.science/pith/2QQJ4VS63M5CJFTVVW4YPWDRDY/action/citation_signature","submit_replication":"https://pith.science/pith/2QQJ4VS63M5CJFTVVW4YPWDRDY/action/replication_record"}},"created_at":"2026-07-05T07:34:02.793084+00:00","updated_at":"2026-07-05T07:34:02.793084+00:00"}