{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:DNRSUO7N2TKPJVOST4HYWCL7H2","short_pith_number":"pith:DNRSUO7N","schema_version":"1.0","canonical_sha256":"1b632a3bedd4d4f4d5d29f0f8b097f3eb1f6835c972ae8469a45a98af1634d15","source":{"kind":"arxiv","id":"1407.7805","version":3},"attestation_state":"computed","paper":{"title":"Monte Carlo sampling from the quantum state space. I","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Berthold-Georg Englert, David John Nott, Hui Khoon Ng, Jiangwei Shang, Yi-Lin Seah","submitted_at":"2014-07-29T18:09:38Z","abstract_excerpt":"High-quality random samples of quantum states are needed for a variety of tasks in quantum information and quantum computation. Searching the high-dimensional quantum state space for a global maximum of an objective function with many local maxima or evaluating an integral over a region in the quantum state space are but two exemplary applications of many. These tasks can only be performed reliably and efficiently with Monte Carlo methods, which involve good samplings of the parameter space in accordance with the relevant target distribution. We show how the standard strategies of rejection sa"},"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":"1407.7805","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2014-07-29T18:09:38Z","cross_cats_sorted":[],"title_canon_sha256":"f7cfa766ab5c79d064501830a262a06d332fc1b46eb972605875aaaed8527bb3","abstract_canon_sha256":"97b21659a34ba5e5e920a45bb6d24c646b5e0421aa1c8255cc4d6c504abda4f8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:17:52.009005Z","signature_b64":"FI2+r/Zd4Gl3QGjn1F4T7NH1GrppvQZ1fI0WyaGN0UQGsLn4lTJq5qRXR8oAlVMb54euxl6ua5dYH9b9hud9Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b632a3bedd4d4f4d5d29f0f8b097f3eb1f6835c972ae8469a45a98af1634d15","last_reissued_at":"2026-05-18T02:17:52.008283Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:17:52.008283Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Monte Carlo sampling from the quantum state space. I","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Berthold-Georg Englert, David John Nott, Hui Khoon Ng, Jiangwei Shang, Yi-Lin Seah","submitted_at":"2014-07-29T18:09:38Z","abstract_excerpt":"High-quality random samples of quantum states are needed for a variety of tasks in quantum information and quantum computation. Searching the high-dimensional quantum state space for a global maximum of an objective function with many local maxima or evaluating an integral over a region in the quantum state space are but two exemplary applications of many. These tasks can only be performed reliably and efficiently with Monte Carlo methods, which involve good samplings of the parameter space in accordance with the relevant target distribution. We show how the standard strategies of rejection sa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1407.7805","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":""},"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":"1407.7805","created_at":"2026-05-18T02:17:52.008417+00:00"},{"alias_kind":"arxiv_version","alias_value":"1407.7805v3","created_at":"2026-05-18T02:17:52.008417+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1407.7805","created_at":"2026-05-18T02:17:52.008417+00:00"},{"alias_kind":"pith_short_12","alias_value":"DNRSUO7N2TKP","created_at":"2026-05-18T12:28:25.294606+00:00"},{"alias_kind":"pith_short_16","alias_value":"DNRSUO7N2TKPJVOS","created_at":"2026-05-18T12:28:25.294606+00:00"},{"alias_kind":"pith_short_8","alias_value":"DNRSUO7N","created_at":"2026-05-18T12:28:25.294606+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/DNRSUO7N2TKPJVOST4HYWCL7H2","json":"https://pith.science/pith/DNRSUO7N2TKPJVOST4HYWCL7H2.json","graph_json":"https://pith.science/api/pith-number/DNRSUO7N2TKPJVOST4HYWCL7H2/graph.json","events_json":"https://pith.science/api/pith-number/DNRSUO7N2TKPJVOST4HYWCL7H2/events.json","paper":"https://pith.science/paper/DNRSUO7N"},"agent_actions":{"view_html":"https://pith.science/pith/DNRSUO7N2TKPJVOST4HYWCL7H2","download_json":"https://pith.science/pith/DNRSUO7N2TKPJVOST4HYWCL7H2.json","view_paper":"https://pith.science/paper/DNRSUO7N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1407.7805&json=true","fetch_graph":"https://pith.science/api/pith-number/DNRSUO7N2TKPJVOST4HYWCL7H2/graph.json","fetch_events":"https://pith.science/api/pith-number/DNRSUO7N2TKPJVOST4HYWCL7H2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DNRSUO7N2TKPJVOST4HYWCL7H2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DNRSUO7N2TKPJVOST4HYWCL7H2/action/storage_attestation","attest_author":"https://pith.science/pith/DNRSUO7N2TKPJVOST4HYWCL7H2/action/author_attestation","sign_citation":"https://pith.science/pith/DNRSUO7N2TKPJVOST4HYWCL7H2/action/citation_signature","submit_replication":"https://pith.science/pith/DNRSUO7N2TKPJVOST4HYWCL7H2/action/replication_record"}},"created_at":"2026-05-18T02:17:52.008417+00:00","updated_at":"2026-05-18T02:17:52.008417+00:00"}