{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TVX4MYBD2FADUE3HFPJPXTLVYN","short_pith_number":"pith:TVX4MYBD","schema_version":"1.0","canonical_sha256":"9d6fc66023d1403a13672bd2fbcd75c3494a69eae94476ad45edb6551f16c14f","source":{"kind":"arxiv","id":"2410.18618","version":1},"attestation_state":"computed","paper":{"title":"Adiabatic training for Variational Quantum Algorithms","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.ET"],"primary_cat":"quant-ph","authors_text":"Carlos Cano Gutierrez, Ernesto Acosta, Guillermo Botella, Roberto Campos","submitted_at":"2024-10-24T10:17:48Z","abstract_excerpt":"This paper presents a new hybrid Quantum Machine Learning (QML) model composed of three elements: a classical computer in charge of the data preparation and interpretation; a Gate-based Quantum Computer running the Variational Quantum Algorithm (VQA) representing the Quantum Neural Network (QNN); and an adiabatic Quantum Computer where the optimization function is executed to find the best parameters for the VQA.\n  As of the moment of this writing, the majority of QNNs are being trained using gradient-based classical optimizers having to deal with the barren-plateau effect. Some gradient-free "},"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":"2410.18618","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"quant-ph","submitted_at":"2024-10-24T10:17:48Z","cross_cats_sorted":["cs.ET"],"title_canon_sha256":"7bbd612121e53f204fb931f68183c94ab48f0d7c467f1205469f56b76f93b88c","abstract_canon_sha256":"06eb61b23c6d59ce0a9deedaa03f73aaf9bbfdf5a9fa13edb413afc955330746"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:25:11.378017Z","signature_b64":"fUXpQqjaZU8yEoCsJMb5YuS2Ibk8/C4BKTipBtUkk0/wUow7fczJN/U5myeW2CoukY8eMUPyI+c4tr6F1pibAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9d6fc66023d1403a13672bd2fbcd75c3494a69eae94476ad45edb6551f16c14f","last_reissued_at":"2026-07-05T09:25:11.377530Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:25:11.377530Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adiabatic training for Variational Quantum Algorithms","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.ET"],"primary_cat":"quant-ph","authors_text":"Carlos Cano Gutierrez, Ernesto Acosta, Guillermo Botella, Roberto Campos","submitted_at":"2024-10-24T10:17:48Z","abstract_excerpt":"This paper presents a new hybrid Quantum Machine Learning (QML) model composed of three elements: a classical computer in charge of the data preparation and interpretation; a Gate-based Quantum Computer running the Variational Quantum Algorithm (VQA) representing the Quantum Neural Network (QNN); and an adiabatic Quantum Computer where the optimization function is executed to find the best parameters for the VQA.\n  As of the moment of this writing, the majority of QNNs are being trained using gradient-based classical optimizers having to deal with the barren-plateau effect. Some gradient-free "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.18618","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/2410.18618/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":"2410.18618","created_at":"2026-07-05T09:25:11.377589+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.18618v1","created_at":"2026-07-05T09:25:11.377589+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.18618","created_at":"2026-07-05T09:25:11.377589+00:00"},{"alias_kind":"pith_short_12","alias_value":"TVX4MYBD2FAD","created_at":"2026-07-05T09:25:11.377589+00:00"},{"alias_kind":"pith_short_16","alias_value":"TVX4MYBD2FADUE3H","created_at":"2026-07-05T09:25:11.377589+00:00"},{"alias_kind":"pith_short_8","alias_value":"TVX4MYBD","created_at":"2026-07-05T09:25:11.377589+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.01821","citing_title":"QUBO-based training for VQAs on Quantum Annealers","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TVX4MYBD2FADUE3HFPJPXTLVYN","json":"https://pith.science/pith/TVX4MYBD2FADUE3HFPJPXTLVYN.json","graph_json":"https://pith.science/api/pith-number/TVX4MYBD2FADUE3HFPJPXTLVYN/graph.json","events_json":"https://pith.science/api/pith-number/TVX4MYBD2FADUE3HFPJPXTLVYN/events.json","paper":"https://pith.science/paper/TVX4MYBD"},"agent_actions":{"view_html":"https://pith.science/pith/TVX4MYBD2FADUE3HFPJPXTLVYN","download_json":"https://pith.science/pith/TVX4MYBD2FADUE3HFPJPXTLVYN.json","view_paper":"https://pith.science/paper/TVX4MYBD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.18618&json=true","fetch_graph":"https://pith.science/api/pith-number/TVX4MYBD2FADUE3HFPJPXTLVYN/graph.json","fetch_events":"https://pith.science/api/pith-number/TVX4MYBD2FADUE3HFPJPXTLVYN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TVX4MYBD2FADUE3HFPJPXTLVYN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TVX4MYBD2FADUE3HFPJPXTLVYN/action/storage_attestation","attest_author":"https://pith.science/pith/TVX4MYBD2FADUE3HFPJPXTLVYN/action/author_attestation","sign_citation":"https://pith.science/pith/TVX4MYBD2FADUE3HFPJPXTLVYN/action/citation_signature","submit_replication":"https://pith.science/pith/TVX4MYBD2FADUE3HFPJPXTLVYN/action/replication_record"}},"created_at":"2026-07-05T09:25:11.377589+00:00","updated_at":"2026-07-05T09:25:11.377589+00:00"}