{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:IXARQGQRC75QWVZFQ734M7POWH","short_pith_number":"pith:IXARQGQR","schema_version":"1.0","canonical_sha256":"45c1181a1117fb0b572587f7c67deeb1d8cbbad491014f5c67e5a35fc650656c","source":{"kind":"arxiv","id":"1812.01041","version":2},"attestation_state":"computed","paper":{"title":"Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn","cond-mat.stat-mech"],"primary_cat":"quant-ph","authors_text":"Hannes Pichler, Leo Zhou, Mikhail D. Lukin, Sheng-Tao Wang, Soonwon Choi","submitted_at":"2018-12-03T19:25:23Z","abstract_excerpt":"The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical variational algorithm designed to tackle combinatorial optimization problems. Despite its promise for near-term quantum applications, not much is currently understood about QAOA's performance beyond its lowest-depth variant. An essential but missing ingredient for understanding and deploying QAOA is a constructive approach to carry out the outer-loop classical optimization. We provide an in-depth study of the performance of QAOA on MaxCut problems by developing an efficient parameter-optimization procedure and "},"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":"1812.01041","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2018-12-03T19:25:23Z","cross_cats_sorted":["cond-mat.dis-nn","cond-mat.stat-mech"],"title_canon_sha256":"d5a310c9604396d56e1ad292371d4c8f7007e315259b2e308bd74d968a0e485c","abstract_canon_sha256":"80688460f6c8226b765e37b8cf5c055d8ea0854ca3b01a6f840aa0820605eeea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:13:22.599083Z","signature_b64":"cYTUMGSX0pJ/Hh2gSQmXC5sKo+Gg3Yb5JIpd4SqCYk0qlAl6KiaZwU5ZxyKtadmVDL7lHe+hw0jiUQknwcnZBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45c1181a1117fb0b572587f7c67deeb1d8cbbad491014f5c67e5a35fc650656c","last_reissued_at":"2026-07-05T01:13:22.598683Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:13:22.598683Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn","cond-mat.stat-mech"],"primary_cat":"quant-ph","authors_text":"Hannes Pichler, Leo Zhou, Mikhail D. Lukin, Sheng-Tao Wang, Soonwon Choi","submitted_at":"2018-12-03T19:25:23Z","abstract_excerpt":"The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical variational algorithm designed to tackle combinatorial optimization problems. Despite its promise for near-term quantum applications, not much is currently understood about QAOA's performance beyond its lowest-depth variant. An essential but missing ingredient for understanding and deploying QAOA is a constructive approach to carry out the outer-loop classical optimization. We provide an in-depth study of the performance of QAOA on MaxCut problems by developing an efficient parameter-optimization procedure and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.01041","kind":"arxiv","version":2},"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/1812.01041/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":"1812.01041","created_at":"2026-07-05T01:13:22.598740+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.01041v2","created_at":"2026-07-05T01:13:22.598740+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.01041","created_at":"2026-07-05T01:13:22.598740+00:00"},{"alias_kind":"pith_short_12","alias_value":"IXARQGQRC75Q","created_at":"2026-07-05T01:13:22.598740+00:00"},{"alias_kind":"pith_short_16","alias_value":"IXARQGQRC75QWVZF","created_at":"2026-07-05T01:13:22.598740+00:00"},{"alias_kind":"pith_short_8","alias_value":"IXARQGQR","created_at":"2026-07-05T01:13:22.598740+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.08707","citing_title":"Simulating quantum circuits with a neural statebank","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"1907.05415","citing_title":"Learning to learn with quantum neural networks via classical neural networks","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"1907.09631","citing_title":"Analysis of Quantum Approximate Optimization Algorithm under Realistic Noise in Superconducting Qubits","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2604.11688","citing_title":"Frustration-Induced Expressibility Limitations in Variational Quantum Algorithms","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IXARQGQRC75QWVZFQ734M7POWH","json":"https://pith.science/pith/IXARQGQRC75QWVZFQ734M7POWH.json","graph_json":"https://pith.science/api/pith-number/IXARQGQRC75QWVZFQ734M7POWH/graph.json","events_json":"https://pith.science/api/pith-number/IXARQGQRC75QWVZFQ734M7POWH/events.json","paper":"https://pith.science/paper/IXARQGQR"},"agent_actions":{"view_html":"https://pith.science/pith/IXARQGQRC75QWVZFQ734M7POWH","download_json":"https://pith.science/pith/IXARQGQRC75QWVZFQ734M7POWH.json","view_paper":"https://pith.science/paper/IXARQGQR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.01041&json=true","fetch_graph":"https://pith.science/api/pith-number/IXARQGQRC75QWVZFQ734M7POWH/graph.json","fetch_events":"https://pith.science/api/pith-number/IXARQGQRC75QWVZFQ734M7POWH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IXARQGQRC75QWVZFQ734M7POWH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IXARQGQRC75QWVZFQ734M7POWH/action/storage_attestation","attest_author":"https://pith.science/pith/IXARQGQRC75QWVZFQ734M7POWH/action/author_attestation","sign_citation":"https://pith.science/pith/IXARQGQRC75QWVZFQ734M7POWH/action/citation_signature","submit_replication":"https://pith.science/pith/IXARQGQRC75QWVZFQ734M7POWH/action/replication_record"}},"created_at":"2026-07-05T01:13:22.598740+00:00","updated_at":"2026-07-05T01:13:22.598740+00:00"}