{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:W6KT3352NJAERGTM6KENOIAIOK","short_pith_number":"pith:W6KT3352","schema_version":"1.0","canonical_sha256":"b7953defba6a40489a6cf288d7200872bc4dd917b9da21bc6b0d4cfad334fe70","source":{"kind":"arxiv","id":"1412.6062","version":2},"attestation_state":"computed","paper":{"title":"A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Edward Farhi, Jeffrey Goldstone, Sam Gutmann","submitted_at":"2014-12-18T20:38:18Z","abstract_excerpt":"We apply our recent Quantum Approximate Optimization Algorithm to the combinatorial problem of bounded occurrence Max E3LIN2. The input is a set of linear equations each of which contains exactly three boolean variables and each equation says that the sum of the variables mod 2 is 0 or is 1. Every variable is in no more than D equations. A random string will satisfy 1/2 of the equations. We show that the level one QAOA will efficiently produce a string that satisfies $\\left(\\frac{1}{2} + \\frac{1}{101 D^{1/2}\\, l n\\, D}\\right)$ times the number of equations. A recent classical algorithm achieve"},"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":"1412.6062","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2014-12-18T20:38:18Z","cross_cats_sorted":[],"title_canon_sha256":"a694b1ef0d926f095cfce90eb225da95b68b6128e73cff822e3397d0ecc0ceb7","abstract_canon_sha256":"d9062c7c5e715d6a6b5223098ec44845e1609b5b8df7a87c1159a6429aa6d791"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:38:54.004551Z","signature_b64":"66/AYLuJ5jGEyeAZ9IhqgKOKcHx0uoJZIJXSQ2ju+HKmIRJ88fBTxDrU+6XbYJU4YyGexvoKt4YZV8EjPxltCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7953defba6a40489a6cf288d7200872bc4dd917b9da21bc6b0d4cfad334fe70","last_reissued_at":"2026-05-18T01:38:54.003914Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:38:54.003914Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Edward Farhi, Jeffrey Goldstone, Sam Gutmann","submitted_at":"2014-12-18T20:38:18Z","abstract_excerpt":"We apply our recent Quantum Approximate Optimization Algorithm to the combinatorial problem of bounded occurrence Max E3LIN2. The input is a set of linear equations each of which contains exactly three boolean variables and each equation says that the sum of the variables mod 2 is 0 or is 1. Every variable is in no more than D equations. A random string will satisfy 1/2 of the equations. We show that the level one QAOA will efficiently produce a string that satisfies $\\left(\\frac{1}{2} + \\frac{1}{101 D^{1/2}\\, l n\\, D}\\right)$ times the number of equations. A recent classical algorithm achieve"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.6062","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":""},"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":"1412.6062","created_at":"2026-05-18T01:38:54.004011+00:00"},{"alias_kind":"arxiv_version","alias_value":"1412.6062v2","created_at":"2026-05-18T01:38:54.004011+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.6062","created_at":"2026-05-18T01:38:54.004011+00:00"},{"alias_kind":"pith_short_12","alias_value":"W6KT3352NJAE","created_at":"2026-05-18T12:28:54.890064+00:00"},{"alias_kind":"pith_short_16","alias_value":"W6KT3352NJAERGTM","created_at":"2026-05-18T12:28:54.890064+00:00"},{"alias_kind":"pith_short_8","alias_value":"W6KT3352","created_at":"2026-05-18T12:28:54.890064+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2605.23138","citing_title":"Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning","ref_index":20,"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":2,"is_internal_anchor":true},{"citing_arxiv_id":"2604.25760","citing_title":"Beyond Single Trajectories: Optimal Control and Jordan-Lie Algebra in Hybrid Quantum Walks for Combinatorial Optimization","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19871","citing_title":"Co-Designing Error Mitigation and Error Detection for Logical Qubits","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07218","citing_title":"Improving Feasibility in Quantum Approximate Optimization Algorithm for Vehicle Routing via Constraint-Aware Initialization and Hybrid XY-X Mixing","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W6KT3352NJAERGTM6KENOIAIOK","json":"https://pith.science/pith/W6KT3352NJAERGTM6KENOIAIOK.json","graph_json":"https://pith.science/api/pith-number/W6KT3352NJAERGTM6KENOIAIOK/graph.json","events_json":"https://pith.science/api/pith-number/W6KT3352NJAERGTM6KENOIAIOK/events.json","paper":"https://pith.science/paper/W6KT3352"},"agent_actions":{"view_html":"https://pith.science/pith/W6KT3352NJAERGTM6KENOIAIOK","download_json":"https://pith.science/pith/W6KT3352NJAERGTM6KENOIAIOK.json","view_paper":"https://pith.science/paper/W6KT3352","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1412.6062&json=true","fetch_graph":"https://pith.science/api/pith-number/W6KT3352NJAERGTM6KENOIAIOK/graph.json","fetch_events":"https://pith.science/api/pith-number/W6KT3352NJAERGTM6KENOIAIOK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W6KT3352NJAERGTM6KENOIAIOK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W6KT3352NJAERGTM6KENOIAIOK/action/storage_attestation","attest_author":"https://pith.science/pith/W6KT3352NJAERGTM6KENOIAIOK/action/author_attestation","sign_citation":"https://pith.science/pith/W6KT3352NJAERGTM6KENOIAIOK/action/citation_signature","submit_replication":"https://pith.science/pith/W6KT3352NJAERGTM6KENOIAIOK/action/replication_record"}},"created_at":"2026-05-18T01:38:54.004011+00:00","updated_at":"2026-05-18T01:38:54.004011+00:00"}