{"id":"W3184170950","doi":"10.1016/j.cpc.2021.108102","title":"Computational overhead of locality reduction in binary optimization problems","year":2021,"lang":"en","type":"preprint","venue":"Computer Physics Communications","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"QLT (Canada)","funders":"","keywords":"Quadratic unconstrained binary optimization; Quantum annealing; Locality; Binary number; Computer science; Solver; Simulated annealing; Reduction (mathematics); Mathematical optimization; Optimization problem; Population; Quantum; Quantum computer; Ising model; Theoretical computer science; Algorithm; Mathematics; Statistical physics; Physics; Quantum mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003973265,0.0003312929,0.000541573,0.0002190617,0.0001946602,0.0002545366,0.002925959,0.0002042148,0.000002550706],"category_scores_gemma":[0.00001252619,0.0003845828,0.0002218372,0.0009768407,0.0002222903,0.0003056444,0.007127636,0.001087353,0.000002767528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001339218,"about_ca_system_score_gemma":0.0004900445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001476232,"about_ca_topic_score_gemma":0.000007294938,"domain_scores_codex":[0.9972209,0.0005774903,0.0008284849,0.0007103056,0.0003964448,0.0002663816],"domain_scores_gemma":[0.9951907,0.0002483414,0.0005932008,0.003381999,0.0005104006,0.00007534817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000136384,0.0006086548,0.00008063389,0.0001500955,0.0000432661,0.000001543149,0.001860763,0.9624754,0.00002118314,0.01014527,0.0000889267,0.02452286],"study_design_scores_gemma":[0.0002582075,0.00003240124,0.001024856,0.0006142113,0.0000119775,0.00001125172,0.0000186926,0.9738702,0.00001783306,0.0237822,0.00003447745,0.0003237419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007856049,0.0007016333,0.9886702,0.001339725,0.000642425,0.000440869,0.00002014597,0.0001811966,0.0001478009],"genre_scores_gemma":[0.317513,0.0001206576,0.6816288,0.00005168117,0.0001189351,0.00003575796,0.0005087031,0.00001888255,0.000003664594],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3096569,"threshold_uncertainty_score":0.9998606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03319884652111329,"score_gpt":0.2789812256709334,"score_spread":0.2457823791498201,"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."}}