{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:SBZRCQ6ACJHE3MJNEMKPGRRVP3","short_pith_number":"pith:SBZRCQ6A","schema_version":"1.0","canonical_sha256":"90731143c0124e4db12d2314f346357ec2558dc2602dda947554ef8344f75904","source":{"kind":"arxiv","id":"1910.04881","version":1},"attestation_state":"computed","paper":{"title":"Evaluating Quantum Approximate Optimization Algorithm: A Case Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"quant-ph","authors_text":"Ruslan Shaydulin, Yuri Alexeev","submitted_at":"2019-10-10T21:14:22Z","abstract_excerpt":"Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising quantum algorithms for the Noisy Intermediate-Scale Quantum (NISQ) era. Quantifying the performance of QAOA in the near-term regime is of utmost importance. We perform a large-scale numerical study of the approximation ratios attainable by QAOA is the low- to medium-depth regime. To find good QAOA parameters we perform 990 million 10-qubit QAOA circuit evaluations. We find that the approximation ratio increases only marginally as the depth is increased, and the gains are offset by the increasing complexity of optimi"},"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":"1910.04881","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2019-10-10T21:14:22Z","cross_cats_sorted":["cs.DS"],"title_canon_sha256":"0a165fd1ebc9463900fee406cc147cd8df1767f3e901b9688118b7d6957b83ee","abstract_canon_sha256":"777ca4cd52f78d043597227b238077ee14d04204becfc32e3d70a13205431db5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:31:55.173775Z","signature_b64":"+06nD5BJa43baLyXZupeu6kp0kspHNN4Cwh20eY2Pd48nfmakrVnPWgw+ooSJgombGa9sCaFAe1CoQcuIXOoCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90731143c0124e4db12d2314f346357ec2558dc2602dda947554ef8344f75904","last_reissued_at":"2026-07-05T04:31:55.173299Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:31:55.173299Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating Quantum Approximate Optimization Algorithm: A Case Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"quant-ph","authors_text":"Ruslan Shaydulin, Yuri Alexeev","submitted_at":"2019-10-10T21:14:22Z","abstract_excerpt":"Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising quantum algorithms for the Noisy Intermediate-Scale Quantum (NISQ) era. Quantifying the performance of QAOA in the near-term regime is of utmost importance. We perform a large-scale numerical study of the approximation ratios attainable by QAOA is the low- to medium-depth regime. To find good QAOA parameters we perform 990 million 10-qubit QAOA circuit evaluations. We find that the approximation ratio increases only marginally as the depth is increased, and the gains are offset by the increasing complexity of optimi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.04881","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/1910.04881/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":"1910.04881","created_at":"2026-07-05T04:31:55.173365+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.04881v1","created_at":"2026-07-05T04:31:55.173365+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.04881","created_at":"2026-07-05T04:31:55.173365+00:00"},{"alias_kind":"pith_short_12","alias_value":"SBZRCQ6ACJHE","created_at":"2026-07-05T04:31:55.173365+00:00"},{"alias_kind":"pith_short_16","alias_value":"SBZRCQ6ACJHE3MJN","created_at":"2026-07-05T04:31:55.173365+00:00"},{"alias_kind":"pith_short_8","alias_value":"SBZRCQ6A","created_at":"2026-07-05T04:31:55.173365+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/SBZRCQ6ACJHE3MJNEMKPGRRVP3","json":"https://pith.science/pith/SBZRCQ6ACJHE3MJNEMKPGRRVP3.json","graph_json":"https://pith.science/api/pith-number/SBZRCQ6ACJHE3MJNEMKPGRRVP3/graph.json","events_json":"https://pith.science/api/pith-number/SBZRCQ6ACJHE3MJNEMKPGRRVP3/events.json","paper":"https://pith.science/paper/SBZRCQ6A"},"agent_actions":{"view_html":"https://pith.science/pith/SBZRCQ6ACJHE3MJNEMKPGRRVP3","download_json":"https://pith.science/pith/SBZRCQ6ACJHE3MJNEMKPGRRVP3.json","view_paper":"https://pith.science/paper/SBZRCQ6A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.04881&json=true","fetch_graph":"https://pith.science/api/pith-number/SBZRCQ6ACJHE3MJNEMKPGRRVP3/graph.json","fetch_events":"https://pith.science/api/pith-number/SBZRCQ6ACJHE3MJNEMKPGRRVP3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SBZRCQ6ACJHE3MJNEMKPGRRVP3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SBZRCQ6ACJHE3MJNEMKPGRRVP3/action/storage_attestation","attest_author":"https://pith.science/pith/SBZRCQ6ACJHE3MJNEMKPGRRVP3/action/author_attestation","sign_citation":"https://pith.science/pith/SBZRCQ6ACJHE3MJNEMKPGRRVP3/action/citation_signature","submit_replication":"https://pith.science/pith/SBZRCQ6ACJHE3MJNEMKPGRRVP3/action/replication_record"}},"created_at":"2026-07-05T04:31:55.173365+00:00","updated_at":"2026-07-05T04:31:55.173365+00:00"}