{"id":"W2562046152","doi":"10.48550/arxiv.1801.08653","title":"Efficient Combinatorial Optimization Using Quantum Annealing","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quantum annealing; Quadratic unconstrained binary optimization; Heuristics; Computer science; Quantum; Quantum computer; Ising model; Simulated annealing; Clique; Graph; Theoretical computer science; Algorithm; Mathematics; Statistical physics; Combinatorics; Quantum mechanics; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003725399,0.0003685677,0.0003541483,0.0003093008,0.0004320383,0.0002477452,0.001685248,0.0003149593,0.00001108969],"category_scores_gemma":[0.00003978798,0.00041736,0.0002099796,0.0007170954,0.0001345085,0.00008627564,0.002557948,0.0005913474,0.00001913755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002053925,"about_ca_system_score_gemma":0.0002579409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001171711,"about_ca_topic_score_gemma":8.967037e-7,"domain_scores_codex":[0.9976742,0.0001986154,0.0002548063,0.001246111,0.0001577282,0.0004685128],"domain_scores_gemma":[0.9980198,0.00008744404,0.0003543061,0.001092412,0.0002682687,0.0001778036],"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":[0.00001137667,0.00006061068,0.00009166094,0.00003327946,0.00003401709,0.00009127976,0.0002005337,0.9703683,0.000006843082,0.02895904,0.00003205057,0.0001109891],"study_design_scores_gemma":[0.0004220256,0.00006222693,0.00003912419,0.0001539565,0.00003774914,0.000009552932,0.00001437217,0.9837989,0.00004110334,0.01491883,0.00003927464,0.0004629086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4087718,0.00002629039,0.5877341,0.00002579504,0.002928003,0.0001436166,0.000005428075,0.0002510596,0.0001139351],"genre_scores_gemma":[0.9578583,0.000009027282,0.04152286,0.00003878297,0.0005092021,2.14595e-7,0.00001064335,0.00002586754,0.00002510185],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5490866,"threshold_uncertainty_score":0.9998278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04575292858148585,"score_gpt":0.1972359380443834,"score_spread":0.1514830094628976,"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."}}