{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:Z6YBAHYT5BD3IHLTNCFBS7XOLL","short_pith_number":"pith:Z6YBAHYT","schema_version":"1.0","canonical_sha256":"cfb0101f13e847b41d73688a197eee5ad4615bba04b48945312498d5b9771c93","source":{"kind":"arxiv","id":"2210.05489","version":3},"attestation_state":"computed","paper":{"title":"Generating Approximate Ground States of Molecules Using Quantum Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Carlos Ortiz Marrero, Jack Ceroni, Juan Miguel Arrazola, Maria Kieferova, Nathan Wiebe, Torin F. Stetina","submitted_at":"2022-10-11T14:45:07Z","abstract_excerpt":"The potential energy surface (PES) of molecules with respect to their nuclear positions is a primary tool in understanding chemical reactions from first principles. However, obtaining this information is complicated by the fact that sampling a large number of ground states over a high-dimensional PES can require a vast number of state preparations. In this work, we propose using a generative quantum machine learning model to prepare quantum states at arbitrary points on the PES. The model is trained using quantum data consisting of ground-state wavefunctions associated with different classical"},"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":"2210.05489","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2022-10-11T14:45:07Z","cross_cats_sorted":[],"title_canon_sha256":"06f3e2e1105505d59c20c1c8783598b3775daed3d6b61ef62cedd4b18200d070","abstract_canon_sha256":"8e311cabe8aa539475bd5b0ecfef04ba71bb23e8e08ee85b5a41158d039a211a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:29:39.712587Z","signature_b64":"MP2b7v2snxClpaibcVNODXOydXI0AWVMbHer6s9rxZzsJwBjziaPrs4GZB8csl83tAd5Js6nzNcShGkrVkTNCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cfb0101f13e847b41d73688a197eee5ad4615bba04b48945312498d5b9771c93","last_reissued_at":"2026-07-05T05:29:39.712111Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:29:39.712111Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generating Approximate Ground States of Molecules Using Quantum Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Carlos Ortiz Marrero, Jack Ceroni, Juan Miguel Arrazola, Maria Kieferova, Nathan Wiebe, Torin F. Stetina","submitted_at":"2022-10-11T14:45:07Z","abstract_excerpt":"The potential energy surface (PES) of molecules with respect to their nuclear positions is a primary tool in understanding chemical reactions from first principles. However, obtaining this information is complicated by the fact that sampling a large number of ground states over a high-dimensional PES can require a vast number of state preparations. In this work, we propose using a generative quantum machine learning model to prepare quantum states at arbitrary points on the PES. The model is trained using quantum data consisting of ground-state wavefunctions associated with different classical"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.05489","kind":"arxiv","version":3},"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/2210.05489/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":"2210.05489","created_at":"2026-07-05T05:29:39.712165+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.05489v3","created_at":"2026-07-05T05:29:39.712165+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.05489","created_at":"2026-07-05T05:29:39.712165+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z6YBAHYT5BD3","created_at":"2026-07-05T05:29:39.712165+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z6YBAHYT5BD3IHLT","created_at":"2026-07-05T05:29:39.712165+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z6YBAHYT","created_at":"2026-07-05T05:29:39.712165+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/Z6YBAHYT5BD3IHLTNCFBS7XOLL","json":"https://pith.science/pith/Z6YBAHYT5BD3IHLTNCFBS7XOLL.json","graph_json":"https://pith.science/api/pith-number/Z6YBAHYT5BD3IHLTNCFBS7XOLL/graph.json","events_json":"https://pith.science/api/pith-number/Z6YBAHYT5BD3IHLTNCFBS7XOLL/events.json","paper":"https://pith.science/paper/Z6YBAHYT"},"agent_actions":{"view_html":"https://pith.science/pith/Z6YBAHYT5BD3IHLTNCFBS7XOLL","download_json":"https://pith.science/pith/Z6YBAHYT5BD3IHLTNCFBS7XOLL.json","view_paper":"https://pith.science/paper/Z6YBAHYT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.05489&json=true","fetch_graph":"https://pith.science/api/pith-number/Z6YBAHYT5BD3IHLTNCFBS7XOLL/graph.json","fetch_events":"https://pith.science/api/pith-number/Z6YBAHYT5BD3IHLTNCFBS7XOLL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z6YBAHYT5BD3IHLTNCFBS7XOLL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z6YBAHYT5BD3IHLTNCFBS7XOLL/action/storage_attestation","attest_author":"https://pith.science/pith/Z6YBAHYT5BD3IHLTNCFBS7XOLL/action/author_attestation","sign_citation":"https://pith.science/pith/Z6YBAHYT5BD3IHLTNCFBS7XOLL/action/citation_signature","submit_replication":"https://pith.science/pith/Z6YBAHYT5BD3IHLTNCFBS7XOLL/action/replication_record"}},"created_at":"2026-07-05T05:29:39.712165+00:00","updated_at":"2026-07-05T05:29:39.712165+00:00"}