{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SMU5YEZGVK4I3Y64EAVJPAOEWJ","short_pith_number":"pith:SMU5YEZG","schema_version":"1.0","canonical_sha256":"9329dc1326aab88de3dc202a9781c4b27f53a233d76b579e9ff1222d8c6565ef","source":{"kind":"arxiv","id":"2405.18989","version":1},"attestation_state":"computed","paper":{"title":"Classification analysis of transition-metal chalcogenides and oxides using quantum machine learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Ashok Kumar, Kurudi V Vedavyasa","submitted_at":"2024-05-29T11:09:16Z","abstract_excerpt":"Quantum machine learning (QML) leverages the potential from machine learning to explore the subtle patterns in huge datasets of complex nature with quantum advantages. This exponentially reduces the time and resources necessary for computations. QML accelerates materials research with active screening of chemical space, identifying novel materials for practical applications and classifying structurally diverse materials given their measured properties. This study analyzes the performance of three efficient quantum machine learning algorithms viz., variational quantum eigen solver (VQE), quantu"},"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":"2405.18989","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2024-05-29T11:09:16Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"109f85aa76dea5750a8adae50f8c534e391021b2b85273e467e10be896d97acc","abstract_canon_sha256":"22a49a34b16d562a446582d9ad6e4cdd9c8fc3a4faf4a0f322449b1ba1afbd17"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:24:46.378826Z","signature_b64":"Eu/pCE6eAm62uPLyHW1KJzd9H64Dt1oI5tnPcOXMuZobT6qGmx9W3Zs0gIuxD2k6/pahOx2QYx34GTmoSASgDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9329dc1326aab88de3dc202a9781c4b27f53a233d76b579e9ff1222d8c6565ef","last_reissued_at":"2026-07-05T08:24:46.378294Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:24:46.378294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Classification analysis of transition-metal chalcogenides and oxides using quantum machine learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Ashok Kumar, Kurudi V Vedavyasa","submitted_at":"2024-05-29T11:09:16Z","abstract_excerpt":"Quantum machine learning (QML) leverages the potential from machine learning to explore the subtle patterns in huge datasets of complex nature with quantum advantages. This exponentially reduces the time and resources necessary for computations. QML accelerates materials research with active screening of chemical space, identifying novel materials for practical applications and classifying structurally diverse materials given their measured properties. This study analyzes the performance of three efficient quantum machine learning algorithms viz., variational quantum eigen solver (VQE), quantu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.18989","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/2405.18989/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":"2405.18989","created_at":"2026-07-05T08:24:46.378375+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.18989v1","created_at":"2026-07-05T08:24:46.378375+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.18989","created_at":"2026-07-05T08:24:46.378375+00:00"},{"alias_kind":"pith_short_12","alias_value":"SMU5YEZGVK4I","created_at":"2026-07-05T08:24:46.378375+00:00"},{"alias_kind":"pith_short_16","alias_value":"SMU5YEZGVK4I3Y64","created_at":"2026-07-05T08:24:46.378375+00:00"},{"alias_kind":"pith_short_8","alias_value":"SMU5YEZG","created_at":"2026-07-05T08:24:46.378375+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.18141","citing_title":"Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SMU5YEZGVK4I3Y64EAVJPAOEWJ","json":"https://pith.science/pith/SMU5YEZGVK4I3Y64EAVJPAOEWJ.json","graph_json":"https://pith.science/api/pith-number/SMU5YEZGVK4I3Y64EAVJPAOEWJ/graph.json","events_json":"https://pith.science/api/pith-number/SMU5YEZGVK4I3Y64EAVJPAOEWJ/events.json","paper":"https://pith.science/paper/SMU5YEZG"},"agent_actions":{"view_html":"https://pith.science/pith/SMU5YEZGVK4I3Y64EAVJPAOEWJ","download_json":"https://pith.science/pith/SMU5YEZGVK4I3Y64EAVJPAOEWJ.json","view_paper":"https://pith.science/paper/SMU5YEZG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.18989&json=true","fetch_graph":"https://pith.science/api/pith-number/SMU5YEZGVK4I3Y64EAVJPAOEWJ/graph.json","fetch_events":"https://pith.science/api/pith-number/SMU5YEZGVK4I3Y64EAVJPAOEWJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SMU5YEZGVK4I3Y64EAVJPAOEWJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SMU5YEZGVK4I3Y64EAVJPAOEWJ/action/storage_attestation","attest_author":"https://pith.science/pith/SMU5YEZGVK4I3Y64EAVJPAOEWJ/action/author_attestation","sign_citation":"https://pith.science/pith/SMU5YEZGVK4I3Y64EAVJPAOEWJ/action/citation_signature","submit_replication":"https://pith.science/pith/SMU5YEZGVK4I3Y64EAVJPAOEWJ/action/replication_record"}},"created_at":"2026-07-05T08:24:46.378375+00:00","updated_at":"2026-07-05T08:24:46.378375+00:00"}