{"id":"W4400877627","doi":"10.1109/access.2024.3431540","title":"Stochastic Simulated Quantum Annealing for Fast Solution of Combinatorial Optimization Problems","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Japan Science and Technology Agency; Japan Society for the Promotion of Science","keywords":"Simulated annealing; Quantum annealing; Computer science; Mathematical optimization; Stochastic optimization; Combinatorial optimization; Annealing (glass); Quantum; Quantum computer; Theoretical computer science; Algorithm; Materials science; Mathematics; Quantum mechanics; Physics","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":[0.0009564619,0.0007001081,0.0009161718,0.0004681094,0.0005953077,0.0006780057,0.0009915975,0.0008203733,0.00266362],"category_scores_gemma":[0.002236776,0.0004863947,0.0007956367,0.0005626367,0.0007225695,0.0006769652,0.000700786,0.001348683,0.0004155065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009726305,"about_ca_system_score_gemma":0.001570091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003994486,"about_ca_topic_score_gemma":0.004606569,"domain_scores_codex":[0.9994032,0.0002766364,0.00002666421,0.00006151615,0.0001788087,0.00005305188],"domain_scores_gemma":[0.9990315,0.0006594104,0.00005965392,0.00009426994,0.0001170243,0.00003800896],"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":[0.00004624801,0.00002932629,0.0002833342,0.00009404626,0.0000470284,0.00003615758,0.00004653696,0.9538637,0.003416159,0.01973421,0.0006742282,0.02172901],"study_design_scores_gemma":[0.000006680629,0.000007003236,0.00002499536,0.000002707086,0.000002685487,0.000003627134,0.000003166371,0.9961048,0.0003951286,0.003036204,0.0004108938,0.000002009887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01258308,0.0002905819,0.9827959,0.0001589543,0.0000410987,0.00006392234,0.0000306361,0.0008376456,0.00319831],"genre_scores_gemma":[0.4645532,0.000415425,0.5324517,0.000134729,0.00003404445,0.0004546621,0.0001475194,0.0002637706,0.001544939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003994486,"threshold_uncertainty_score":0.008910656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02316529880778273,"score_gpt":0.2923015483025688,"score_spread":0.269136249494786,"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."}}