{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JHTBEB3YPEC2M5GEF2PJ5BU3J6","short_pith_number":"pith:JHTBEB3Y","schema_version":"1.0","canonical_sha256":"49e61207787905a674c42e9e9e869b4fbe542fea8b9b32db4e189c774d7499c9","source":{"kind":"arxiv","id":"2412.18979","version":1},"attestation_state":"computed","paper":{"title":"Quantum memristors for neuromorphic quantum machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"quant-ph","authors_text":"Lucas Lamata","submitted_at":"2024-12-25T20:21:24Z","abstract_excerpt":"Quantum machine learning may permit to realize more efficient machine learning calculations with near-term quantum devices. Among the diverse quantum machine learning paradigms which are currently being considered, quantum memristors are promising as a way of combining, in the same quantum hardware, a unitary evolution with the nonlinearity provided by the measurement and feedforward. Thus, an efficient way of deploying neuromorphic quantum computing for quantum machine learning may be enabled."},"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":"2412.18979","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2024-12-25T20:21:24Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"4ebacd92048e6dea15a8e6155934423e8cf8c77661c65cdde3ef1924709fbd2f","abstract_canon_sha256":"b7603b3bb551ba0673294291147add01970685f19edb8af4dbdb78239497302f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:14.245739Z","signature_b64":"KFQDBx7dVMzXg+hU34FiN+xw7pjtLVuONvMeAd1LPXIPrdkFz6m1RMfnp45Bz5jk+U683MVRKKhfu0es3DT9Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49e61207787905a674c42e9e9e869b4fbe542fea8b9b32db4e189c774d7499c9","last_reissued_at":"2026-07-05T09:54:14.245333Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:14.245333Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum memristors for neuromorphic quantum machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"quant-ph","authors_text":"Lucas Lamata","submitted_at":"2024-12-25T20:21:24Z","abstract_excerpt":"Quantum machine learning may permit to realize more efficient machine learning calculations with near-term quantum devices. Among the diverse quantum machine learning paradigms which are currently being considered, quantum memristors are promising as a way of combining, in the same quantum hardware, a unitary evolution with the nonlinearity provided by the measurement and feedforward. Thus, an efficient way of deploying neuromorphic quantum computing for quantum machine learning may be enabled."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.18979","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/2412.18979/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":"2412.18979","created_at":"2026-07-05T09:54:14.245388+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.18979v1","created_at":"2026-07-05T09:54:14.245388+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.18979","created_at":"2026-07-05T09:54:14.245388+00:00"},{"alias_kind":"pith_short_12","alias_value":"JHTBEB3YPEC2","created_at":"2026-07-05T09:54:14.245388+00:00"},{"alias_kind":"pith_short_16","alias_value":"JHTBEB3YPEC2M5GE","created_at":"2026-07-05T09:54:14.245388+00:00"},{"alias_kind":"pith_short_8","alias_value":"JHTBEB3Y","created_at":"2026-07-05T09:54:14.245388+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/JHTBEB3YPEC2M5GEF2PJ5BU3J6","json":"https://pith.science/pith/JHTBEB3YPEC2M5GEF2PJ5BU3J6.json","graph_json":"https://pith.science/api/pith-number/JHTBEB3YPEC2M5GEF2PJ5BU3J6/graph.json","events_json":"https://pith.science/api/pith-number/JHTBEB3YPEC2M5GEF2PJ5BU3J6/events.json","paper":"https://pith.science/paper/JHTBEB3Y"},"agent_actions":{"view_html":"https://pith.science/pith/JHTBEB3YPEC2M5GEF2PJ5BU3J6","download_json":"https://pith.science/pith/JHTBEB3YPEC2M5GEF2PJ5BU3J6.json","view_paper":"https://pith.science/paper/JHTBEB3Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.18979&json=true","fetch_graph":"https://pith.science/api/pith-number/JHTBEB3YPEC2M5GEF2PJ5BU3J6/graph.json","fetch_events":"https://pith.science/api/pith-number/JHTBEB3YPEC2M5GEF2PJ5BU3J6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JHTBEB3YPEC2M5GEF2PJ5BU3J6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JHTBEB3YPEC2M5GEF2PJ5BU3J6/action/storage_attestation","attest_author":"https://pith.science/pith/JHTBEB3YPEC2M5GEF2PJ5BU3J6/action/author_attestation","sign_citation":"https://pith.science/pith/JHTBEB3YPEC2M5GEF2PJ5BU3J6/action/citation_signature","submit_replication":"https://pith.science/pith/JHTBEB3YPEC2M5GEF2PJ5BU3J6/action/replication_record"}},"created_at":"2026-07-05T09:54:14.245388+00:00","updated_at":"2026-07-05T09:54:14.245388+00:00"}