{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:OGLOKFZM7FHFMOFSVAAFRW6XSK","short_pith_number":"pith:OGLOKFZM","schema_version":"1.0","canonical_sha256":"7196e5172cf94e5638b2a80058dbd79285efb3e6cd194e1574d8297e462f3959","source":{"kind":"arxiv","id":"1812.04170","version":1},"attestation_state":"computed","paper":{"title":"For Fixed Control Parameters the Quantum Approximate Optimization Algorithm's Objective Function Value Concentrates for Typical Instances","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Edward Farhi, Fernando G.S.L. Brandao, Hartmut Neven, Michael Broughton, Sam Gutmann","submitted_at":"2018-12-11T01:03:58Z","abstract_excerpt":"The Quantum Approximate Optimization Algorithm, QAOA, uses a shallow depth quantum circuit to produce a parameter dependent state. For a given combinatorial optimization problem instance, the quantum expectation of the associated cost function is the parameter dependent objective function of the QAOA. We demonstrate that if the parameters are fixed and the instance comes from a reasonable distribution then the objective function value is concentrated in the sense that typical instances have (nearly) the same value of the objective function. This applies not just for optimal parameters as the w"},"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":"1812.04170","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2018-12-11T01:03:58Z","cross_cats_sorted":[],"title_canon_sha256":"4532847f1a1133628e72715de512c88f532f12f447aa8bd474f6c713cce16a5c","abstract_canon_sha256":"2e20268d83d883219e12a595b606850a3562f6874987a738c549c204b6d1e68a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:35.380464Z","signature_b64":"J+Xd9IJ2kV7CkGpiCQRSh9GdBC6rkPMc7TgP34e3YbK02FUYBYzVwCKZM7YoTHzSZSNRM3HHw6LWYpJ+C1amAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7196e5172cf94e5638b2a80058dbd79285efb3e6cd194e1574d8297e462f3959","last_reissued_at":"2026-05-17T23:58:35.379879Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:35.379879Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"For Fixed Control Parameters the Quantum Approximate Optimization Algorithm's Objective Function Value Concentrates for Typical Instances","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Edward Farhi, Fernando G.S.L. Brandao, Hartmut Neven, Michael Broughton, Sam Gutmann","submitted_at":"2018-12-11T01:03:58Z","abstract_excerpt":"The Quantum Approximate Optimization Algorithm, QAOA, uses a shallow depth quantum circuit to produce a parameter dependent state. For a given combinatorial optimization problem instance, the quantum expectation of the associated cost function is the parameter dependent objective function of the QAOA. We demonstrate that if the parameters are fixed and the instance comes from a reasonable distribution then the objective function value is concentrated in the sense that typical instances have (nearly) the same value of the objective function. This applies not just for optimal parameters as the w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.04170","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":""},"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":"1812.04170","created_at":"2026-05-17T23:58:35.379979+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.04170v1","created_at":"2026-05-17T23:58:35.379979+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.04170","created_at":"2026-05-17T23:58:35.379979+00:00"},{"alias_kind":"pith_short_12","alias_value":"OGLOKFZM7FHF","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"OGLOKFZM7FHFMOFS","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"OGLOKFZM","created_at":"2026-05-18T12:32:43.782077+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":19,"internal_anchor_count":13,"sample":[{"citing_arxiv_id":"2605.23377","citing_title":"SAFE ma-QAOA: Surrogate-Assisted and Fine-Tuning Enhanced Multi-Angle QAOA with Parameter Distillation","ref_index":27,"is_internal_anchor":true},{"citing_arxiv_id":"1907.05415","citing_title":"Learning to learn with quantum neural networks via classical neural networks","ref_index":70,"is_internal_anchor":true},{"citing_arxiv_id":"1907.09631","citing_title":"Analysis of Quantum Approximate Optimization Algorithm under Realistic Noise in Superconducting Qubits","ref_index":10,"is_internal_anchor":true},{"citing_arxiv_id":"2404.19497","citing_title":"Light Cone Cancellation for Variational Quantum Eigensolver in Solving Noisy Max-Cut","ref_index":27,"is_internal_anchor":true},{"citing_arxiv_id":"2504.01694","citing_title":"Iterative Interpolation Schedules for Quantum Approximate Optimization Algorithm","ref_index":45,"is_internal_anchor":true},{"citing_arxiv_id":"2510.19928","citing_title":"Mind the gaps: The fraught road to quantum advantage","ref_index":133,"is_internal_anchor":true},{"citing_arxiv_id":"2605.20288","citing_title":"Mechanism of Efficacy in QAOA for Random k-SAT: From Adiabatic Manifold to Sublinear Parameter Optimization","ref_index":34,"is_internal_anchor":true},{"citing_arxiv_id":"2605.18985","citing_title":"Efficient Fourier-Based Linear Combination of Unitaries and Applications in Quantum Optimization","ref_index":61,"is_internal_anchor":true},{"citing_arxiv_id":"2507.10908","citing_title":"Optimisation-Free Recursive QAOA for the Binary Paint Shop Problem","ref_index":64,"is_internal_anchor":true},{"citing_arxiv_id":"2509.13528","citing_title":"Evaluating the Limits of QAOA Parameter Transfer at High-Rounds on Sparse Ising Models With Geometrically Local Cubic Terms","ref_index":29,"is_internal_anchor":true},{"citing_arxiv_id":"2510.08153","citing_title":"Going off Pattern? QAOA Parameter Heuristics and Potentials of Parsimony","ref_index":6,"is_internal_anchor":true},{"citing_arxiv_id":"2510.19928","citing_title":"Mind the gaps: The fraught road to quantum advantage","ref_index":133,"is_internal_anchor":true},{"citing_arxiv_id":"2602.16141","citing_title":"Reductions of QAOA Induced by Classical Symmetries: Theoretical Insights and Practical Implications","ref_index":8,"is_internal_anchor":true},{"citing_arxiv_id":"2604.27324","citing_title":"Q3SAT-GPT: A Generative Model for Discovering Quantum Circuits for the 3-SAT Problem","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10327","citing_title":"SCALAR: A Neurosymbolic Framework for Automated Conjecture and Reasoning in Quantum Circuit Analysis","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2604.26040","citing_title":"QAOA Parameter Transfer for Hypergraphs","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25275","citing_title":"Graph-Conditioned Meta-Optimizer for QAOA Parameter Generation on Multiple Problem Classes","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20180","citing_title":"Tensor network surrogate models for variational quantum computation","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2604.24803","citing_title":"Query-Efficient Quantum Approximate Optimization via Graph-Conditioned Trust Regions","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OGLOKFZM7FHFMOFSVAAFRW6XSK","json":"https://pith.science/pith/OGLOKFZM7FHFMOFSVAAFRW6XSK.json","graph_json":"https://pith.science/api/pith-number/OGLOKFZM7FHFMOFSVAAFRW6XSK/graph.json","events_json":"https://pith.science/api/pith-number/OGLOKFZM7FHFMOFSVAAFRW6XSK/events.json","paper":"https://pith.science/paper/OGLOKFZM"},"agent_actions":{"view_html":"https://pith.science/pith/OGLOKFZM7FHFMOFSVAAFRW6XSK","download_json":"https://pith.science/pith/OGLOKFZM7FHFMOFSVAAFRW6XSK.json","view_paper":"https://pith.science/paper/OGLOKFZM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.04170&json=true","fetch_graph":"https://pith.science/api/pith-number/OGLOKFZM7FHFMOFSVAAFRW6XSK/graph.json","fetch_events":"https://pith.science/api/pith-number/OGLOKFZM7FHFMOFSVAAFRW6XSK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OGLOKFZM7FHFMOFSVAAFRW6XSK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OGLOKFZM7FHFMOFSVAAFRW6XSK/action/storage_attestation","attest_author":"https://pith.science/pith/OGLOKFZM7FHFMOFSVAAFRW6XSK/action/author_attestation","sign_citation":"https://pith.science/pith/OGLOKFZM7FHFMOFSVAAFRW6XSK/action/citation_signature","submit_replication":"https://pith.science/pith/OGLOKFZM7FHFMOFSVAAFRW6XSK/action/replication_record"}},"created_at":"2026-05-17T23:58:35.379979+00:00","updated_at":"2026-05-17T23:58:35.379979+00:00"}