{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:K3XKAGY5GJLSHYRDCQIRMHOL4F","short_pith_number":"pith:K3XKAGY5","schema_version":"1.0","canonical_sha256":"56eea01b1d325723e2231411161dcbe145494b6730d65392e6739ee1f1c396c6","source":{"kind":"arxiv","id":"2305.13594","version":3},"attestation_state":"computed","paper":{"title":"Connecting the Hamiltonian structure to the QAOA energy and Fourier landscape structure","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Boniface Yogendran, Enrico Fontana, Lanruo Gao, Manuel Rudolph, Micha{\\l} St\\k{e}ch{\\l}y","submitted_at":"2023-05-23T01:56:33Z","abstract_excerpt":"In this paper, we aim to expand the understanding of the relationship between the composition of the Hamiltonian in the Quantum Approximate Optimization Algorithm (QAOA) and the corresponding cost landscape characteristics. QAOA is a prominent example of a Variational Quantum Algorithm (VQA), which is most commonly used for combinatorial optimization. The success of QAOA heavily relies on parameter optimization, which is a great challenge, especially on scarce noisy quantum hardware. Thus understanding the cost function landscape can aid in designing better optimization heuristics and therefor"},"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":"2305.13594","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2023-05-23T01:56:33Z","cross_cats_sorted":[],"title_canon_sha256":"ea38e703dc3ed2d4f1cb2f73cb0ecf8bbe05c07e5fe6648edc448f3b28181d09","abstract_canon_sha256":"eaad635cc100384ab9d9861ec908dc3a5b12088fec05d55c5f9b785bf76131bf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:20:22.830645Z","signature_b64":"FyXD4FBxrvWUnF1wibyqGRAeapdvZZc/X0f1gy/Nod/E137VbRjC7292Kw6j+52z40qwuE1213hAVTUGJIiQBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"56eea01b1d325723e2231411161dcbe145494b6730d65392e6739ee1f1c396c6","last_reissued_at":"2026-07-05T08:20:22.830132Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:20:22.830132Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Connecting the Hamiltonian structure to the QAOA energy and Fourier landscape structure","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Boniface Yogendran, Enrico Fontana, Lanruo Gao, Manuel Rudolph, Micha{\\l} St\\k{e}ch{\\l}y","submitted_at":"2023-05-23T01:56:33Z","abstract_excerpt":"In this paper, we aim to expand the understanding of the relationship between the composition of the Hamiltonian in the Quantum Approximate Optimization Algorithm (QAOA) and the corresponding cost landscape characteristics. QAOA is a prominent example of a Variational Quantum Algorithm (VQA), which is most commonly used for combinatorial optimization. The success of QAOA heavily relies on parameter optimization, which is a great challenge, especially on scarce noisy quantum hardware. Thus understanding the cost function landscape can aid in designing better optimization heuristics and therefor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.13594","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/2305.13594/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":"2305.13594","created_at":"2026-07-05T08:20:22.830197+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.13594v3","created_at":"2026-07-05T08:20:22.830197+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.13594","created_at":"2026-07-05T08:20:22.830197+00:00"},{"alias_kind":"pith_short_12","alias_value":"K3XKAGY5GJLS","created_at":"2026-07-05T08:20:22.830197+00:00"},{"alias_kind":"pith_short_16","alias_value":"K3XKAGY5GJLSHYRD","created_at":"2026-07-05T08:20:22.830197+00:00"},{"alias_kind":"pith_short_8","alias_value":"K3XKAGY5","created_at":"2026-07-05T08:20:22.830197+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2306.11060","citing_title":"PCA and t-SNE analysis in the study of QAOA entangled and non-entangled mixing operators","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K3XKAGY5GJLSHYRDCQIRMHOL4F","json":"https://pith.science/pith/K3XKAGY5GJLSHYRDCQIRMHOL4F.json","graph_json":"https://pith.science/api/pith-number/K3XKAGY5GJLSHYRDCQIRMHOL4F/graph.json","events_json":"https://pith.science/api/pith-number/K3XKAGY5GJLSHYRDCQIRMHOL4F/events.json","paper":"https://pith.science/paper/K3XKAGY5"},"agent_actions":{"view_html":"https://pith.science/pith/K3XKAGY5GJLSHYRDCQIRMHOL4F","download_json":"https://pith.science/pith/K3XKAGY5GJLSHYRDCQIRMHOL4F.json","view_paper":"https://pith.science/paper/K3XKAGY5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.13594&json=true","fetch_graph":"https://pith.science/api/pith-number/K3XKAGY5GJLSHYRDCQIRMHOL4F/graph.json","fetch_events":"https://pith.science/api/pith-number/K3XKAGY5GJLSHYRDCQIRMHOL4F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K3XKAGY5GJLSHYRDCQIRMHOL4F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K3XKAGY5GJLSHYRDCQIRMHOL4F/action/storage_attestation","attest_author":"https://pith.science/pith/K3XKAGY5GJLSHYRDCQIRMHOL4F/action/author_attestation","sign_citation":"https://pith.science/pith/K3XKAGY5GJLSHYRDCQIRMHOL4F/action/citation_signature","submit_replication":"https://pith.science/pith/K3XKAGY5GJLSHYRDCQIRMHOL4F/action/replication_record"}},"created_at":"2026-07-05T08:20:22.830197+00:00","updated_at":"2026-07-05T08:20:22.830197+00:00"}