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If p grows with the input size a different strategy is proposed. We study the algorithm as applied to MaxCut on regular graphs and analyze its performance on 2-regular and 3-regular graphs for fixed p. For p = 1, on 3-regular graphs the quantum algorithm always finds a cut that is at least 0.6924 times the size of the optimal cut.","external_url":"https://arxiv.org/abs/1411.4028","cited_by_count":1803,"metadata_source":"pith","metadata_fetched_at":"2026-08-05T02:28:24.338817+00:00","pith_arxiv_id":"1411.4028","created_at":"2026-05-08T21:44:15.204041+00:00","updated_at":"2026-08-05T02:28:24.338817+00:00","title_quality_ok":true,"display_title":"A Quantum Approximate Optimization Algorithm","render_title":"A Quantum Approximate Optimization Algorithm"},"hub":{"state":{"work_id":"5a33d9f3-407a-4c7e-a119-ff581c66b173","tier":"super_hub","tier_reason":"100+ Pith inbound or 10,000+ external citations","pith_inbound_count":318,"external_cited_by_count":1803,"distinct_field_count":24,"first_pith_cited_at":"2018-11-12T19:18:57+00:00","last_pith_cited_at":"2026-07-09T17:18:19+00:00","author_build_status":"needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-08-23T21:19:22.871683+00:00","tier_text":"super_hub"},"tier":"super_hub","role_counts":[{"context_role":"background","n":42},{"context_role":"method","n":5},{"context_role":"dataset","n":3}],"polarity_counts":[{"context_polarity":"background","n":39},{"context_polarity":"use_method","n":5},{"context_polarity":"support","n":3},{"context_polarity":"use_dataset","n":3}],"runs":{"ask_index":{"job_type":"ask_index","status":"succeeded","result":{"title":"A Quantum Approximate Optimization Algorithm","claims":[{"claim_text":"We introduce a quantum algorithm that produces approximate solutions for combinatorial optimization problems. 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