{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2010:FCV2LITAB3S6WSYH56UHFCHDAH","short_pith_number":"pith:FCV2LITA","schema_version":"1.0","canonical_sha256":"28aba5a2600ee5eb4b07efa87288e301c3c702310f1173ecd844e9061c7ad423","source":{"kind":"arxiv","id":"1004.2468","version":1},"attestation_state":"computed","paper":{"title":"Quantum learning: optimal classification of qubit states","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"quant-ph","authors_text":"Madalin Guta, Wojciech Kotlowski","submitted_at":"2010-04-14T18:30:19Z","abstract_excerpt":"Pattern recognition is a central topic in Learning Theory with numerous applications  such as voice and text recognition, image analysis, computer diagnosis. The statistical set-up in classification is the following: we are given an i.i.d. training set $(X_{1},Y_{1}),... (X_{n},Y_{n})$ where $X_{i}$ represents a feature and $Y_{i}\\in \\{0,1\\}$ is a label attached to that feature. The underlying joint  distribution of $(X,Y)$ is unknown, but we can learn about it from the training set and we aim at devising low error classifiers $f:X\\to Y$ used to predict the label of new incoming features.\n Her"},"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":"1004.2468","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2010-04-14T18:30:19Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"897db4f6aa9ec448752abf498b9b1541f6f501fbfcaa8bf1f0473624c2874f8a","abstract_canon_sha256":"759060f84f6f09fdfb6a0de537cc58271b91b68fdefb1fa7f0998665eddb718a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:19:33.543342Z","signature_b64":"LKnoXiuP82CT6/U4bCdRGqXFp0j4e6X9saa/fZ50pshfYzDerkbarZ6O/m9+CCIXD9F8j6Y32x1vmIYd9YAIAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"28aba5a2600ee5eb4b07efa87288e301c3c702310f1173ecd844e9061c7ad423","last_reissued_at":"2026-05-18T04:19:33.542950Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:19:33.542950Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum learning: optimal classification of qubit states","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"quant-ph","authors_text":"Madalin Guta, Wojciech Kotlowski","submitted_at":"2010-04-14T18:30:19Z","abstract_excerpt":"Pattern recognition is a central topic in Learning Theory with numerous applications  such as voice and text recognition, image analysis, computer diagnosis. The statistical set-up in classification is the following: we are given an i.i.d. training set $(X_{1},Y_{1}),... (X_{n},Y_{n})$ where $X_{i}$ represents a feature and $Y_{i}\\in \\{0,1\\}$ is a label attached to that feature. The underlying joint  distribution of $(X,Y)$ is unknown, but we can learn about it from the training set and we aim at devising low error classifiers $f:X\\to Y$ used to predict the label of new incoming features.\n Her"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1004.2468","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":""},"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":"1004.2468","created_at":"2026-05-18T04:19:33.543001+00:00"},{"alias_kind":"arxiv_version","alias_value":"1004.2468v1","created_at":"2026-05-18T04:19:33.543001+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1004.2468","created_at":"2026-05-18T04:19:33.543001+00:00"},{"alias_kind":"pith_short_12","alias_value":"FCV2LITAB3S6","created_at":"2026-05-18T12:26:06.534383+00:00"},{"alias_kind":"pith_short_16","alias_value":"FCV2LITAB3S6WSYH","created_at":"2026-05-18T12:26:06.534383+00:00"},{"alias_kind":"pith_short_8","alias_value":"FCV2LITA","created_at":"2026-05-18T12:26:06.534383+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/FCV2LITAB3S6WSYH56UHFCHDAH","json":"https://pith.science/pith/FCV2LITAB3S6WSYH56UHFCHDAH.json","graph_json":"https://pith.science/api/pith-number/FCV2LITAB3S6WSYH56UHFCHDAH/graph.json","events_json":"https://pith.science/api/pith-number/FCV2LITAB3S6WSYH56UHFCHDAH/events.json","paper":"https://pith.science/paper/FCV2LITA"},"agent_actions":{"view_html":"https://pith.science/pith/FCV2LITAB3S6WSYH56UHFCHDAH","download_json":"https://pith.science/pith/FCV2LITAB3S6WSYH56UHFCHDAH.json","view_paper":"https://pith.science/paper/FCV2LITA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1004.2468&json=true","fetch_graph":"https://pith.science/api/pith-number/FCV2LITAB3S6WSYH56UHFCHDAH/graph.json","fetch_events":"https://pith.science/api/pith-number/FCV2LITAB3S6WSYH56UHFCHDAH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FCV2LITAB3S6WSYH56UHFCHDAH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FCV2LITAB3S6WSYH56UHFCHDAH/action/storage_attestation","attest_author":"https://pith.science/pith/FCV2LITAB3S6WSYH56UHFCHDAH/action/author_attestation","sign_citation":"https://pith.science/pith/FCV2LITAB3S6WSYH56UHFCHDAH/action/citation_signature","submit_replication":"https://pith.science/pith/FCV2LITAB3S6WSYH56UHFCHDAH/action/replication_record"}},"created_at":"2026-05-18T04:19:33.543001+00:00","updated_at":"2026-05-18T04:19:33.543001+00:00"}