{"id":"W2582746905","doi":"10.1007/s11128-017-1639-2","title":"A subgradient approach for constrained binary optimization via quantum adiabatic evolution","year":2017,"lang":"en","type":"preprint","venue":"Quantum Information Processing","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"QLT (Canada)","funders":"","keywords":"Subgradient method; Quadratic unconstrained binary optimization; Mathematical optimization; Quantum; Lagrangian relaxation; Quadratic programming; Quadratic equation; Adiabatic process; Quantum phase estimation algorithm; Applied mathematics; Computer science; Quantum computer; Binary number; Mathematics; Quantum annealing; Physics; Quantum mechanics; Quantum error correction","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001071884,0.0008317176,0.001005993,0.0005102139,0.0005105411,0.0009677579,0.001295655,0.001135017,0.004159389],"category_scores_gemma":[0.002893059,0.0005162162,0.0005548378,0.000791498,0.001314814,0.001286692,0.001905813,0.001825366,0.0006200375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00100003,"about_ca_system_score_gemma":0.001408547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003504327,"about_ca_topic_score_gemma":0.004193633,"domain_scores_codex":[0.9996201,0.0001863995,0.0000130703,0.00003574753,0.0001159636,0.00002869276],"domain_scores_gemma":[0.9994161,0.0003670744,0.00002926641,0.00005611232,0.00008883667,0.00004253261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001045334,0.000080225,0.0001673784,0.000146824,0.00005652926,0.00005483066,0.00009965441,0.4302736,0.003108035,0.51802,0.004268718,0.04361966],"study_design_scores_gemma":[0.000009818496,0.00001189432,0.00002724874,0.000005505823,0.000003458469,0.000006285377,0.000004665905,0.9515529,0.0002424861,0.04727048,0.0008604502,0.000004654074],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005487448,0.0002542806,0.9870911,0.0003928178,0.00008899799,0.00004460213,0.00004501355,0.0000909355,0.006504796],"genre_scores_gemma":[0.2909912,0.000691736,0.6916101,0.0004696267,0.0002086998,0.0003385083,0.0001671556,0.0003279907,0.01519504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004159389,"threshold_uncertainty_score":0.01391453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01593767461817021,"score_gpt":0.2488040224145937,"score_spread":0.2328663477964235,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}