{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:C4RI5QHRBDJAGOSWQZU73PQ5AP","short_pith_number":"pith:C4RI5QHR","schema_version":"1.0","canonical_sha256":"17228ec0f108d2033a568669fdbe1d03f025a03ad9d33ed66e4dd33e750468b4","source":{"kind":"arxiv","id":"2407.00241","version":3},"attestation_state":"computed","paper":{"title":"Interior Point Methods for Structured Quantum Relative Entropy Optimization Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","math.OC"],"primary_cat":"quant-ph","authors_text":"Hamza Fawzi, James Saunderson, Kerry He","submitted_at":"2024-06-28T21:37:45Z","abstract_excerpt":"Quantum relative entropy optimization refers to a class of convex problems in which a linear functional is minimized over an affine section of the epigraph of the quantum relative entropy function. Recently, the self-concordance of a natural barrier function was proved for this set, and various implementations of interior-point methods have been made available to solve this class of optimization problems. In this paper, we show how common structures arising from applications in quantum information theory can be exploited to improve the efficiency of solving quantum relative entropy optimizatio"},"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":"2407.00241","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2024-06-28T21:37:45Z","cross_cats_sorted":["cs.IT","math.IT","math.OC"],"title_canon_sha256":"1cb8434a21e9f3d47ae97bb4589e0d926439db14794f7c1e64015c9584db3ed5","abstract_canon_sha256":"0fc33590f6cb622b45f8a500451145ca9a035d94b68976280fa86c642759eb28"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:23.558019Z","signature_b64":"fW3aXORI51xZgyMAds6tMtuXbjt/rp2RV3P0e5jI/KlhuIop0Iq8zisoM8s0ikrwbB3un3v4ELIePpO0NeHDCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17228ec0f108d2033a568669fdbe1d03f025a03ad9d33ed66e4dd33e750468b4","last_reissued_at":"2026-07-05T10:51:23.557554Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:23.557554Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Interior Point Methods for Structured Quantum Relative Entropy Optimization Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","math.OC"],"primary_cat":"quant-ph","authors_text":"Hamza Fawzi, James Saunderson, Kerry He","submitted_at":"2024-06-28T21:37:45Z","abstract_excerpt":"Quantum relative entropy optimization refers to a class of convex problems in which a linear functional is minimized over an affine section of the epigraph of the quantum relative entropy function. Recently, the self-concordance of a natural barrier function was proved for this set, and various implementations of interior-point methods have been made available to solve this class of optimization problems. In this paper, we show how common structures arising from applications in quantum information theory can be exploited to improve the efficiency of solving quantum relative entropy optimizatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.00241","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/2407.00241/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":"2407.00241","created_at":"2026-07-05T10:51:23.557610+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.00241v3","created_at":"2026-07-05T10:51:23.557610+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.00241","created_at":"2026-07-05T10:51:23.557610+00:00"},{"alias_kind":"pith_short_12","alias_value":"C4RI5QHRBDJA","created_at":"2026-07-05T10:51:23.557610+00:00"},{"alias_kind":"pith_short_16","alias_value":"C4RI5QHRBDJAGOSW","created_at":"2026-07-05T10:51:23.557610+00:00"},{"alias_kind":"pith_short_8","alias_value":"C4RI5QHR","created_at":"2026-07-05T10:51:23.557610+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2502.02319","citing_title":"Generalized Numerical Framework for Improved Finite-Sized Key Rates with R\\'enyi Entropy","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2511.10584","citing_title":"Finite-size quantum key distribution rates from R\\'enyi entropies using conic optimization","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C4RI5QHRBDJAGOSWQZU73PQ5AP","json":"https://pith.science/pith/C4RI5QHRBDJAGOSWQZU73PQ5AP.json","graph_json":"https://pith.science/api/pith-number/C4RI5QHRBDJAGOSWQZU73PQ5AP/graph.json","events_json":"https://pith.science/api/pith-number/C4RI5QHRBDJAGOSWQZU73PQ5AP/events.json","paper":"https://pith.science/paper/C4RI5QHR"},"agent_actions":{"view_html":"https://pith.science/pith/C4RI5QHRBDJAGOSWQZU73PQ5AP","download_json":"https://pith.science/pith/C4RI5QHRBDJAGOSWQZU73PQ5AP.json","view_paper":"https://pith.science/paper/C4RI5QHR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.00241&json=true","fetch_graph":"https://pith.science/api/pith-number/C4RI5QHRBDJAGOSWQZU73PQ5AP/graph.json","fetch_events":"https://pith.science/api/pith-number/C4RI5QHRBDJAGOSWQZU73PQ5AP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C4RI5QHRBDJAGOSWQZU73PQ5AP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C4RI5QHRBDJAGOSWQZU73PQ5AP/action/storage_attestation","attest_author":"https://pith.science/pith/C4RI5QHRBDJAGOSWQZU73PQ5AP/action/author_attestation","sign_citation":"https://pith.science/pith/C4RI5QHRBDJAGOSWQZU73PQ5AP/action/citation_signature","submit_replication":"https://pith.science/pith/C4RI5QHRBDJAGOSWQZU73PQ5AP/action/replication_record"}},"created_at":"2026-07-05T10:51:23.557610+00:00","updated_at":"2026-07-05T10:51:23.557610+00:00"}