{"id":"W4390846320","doi":"10.1007/978-3-031-42478-6_26","title":"Ising Machines Using Parallel Spin Updating Algorithms for Solving Traveling Salesman Problems","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Travelling salesman problem; Ising model; Simulated annealing; Computer science; Solver; Spins; Computation; Ising spin; Algorithm; Time complexity; Parallel computing; Mathematical optimization; Mathematics; Physics; Statistical 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009131831,0.0008451417,0.0008430996,0.0004051433,0.0008893939,0.0007492728,0.001471366,0.0004158576,0.00001523993],"category_scores_gemma":[0.00006168809,0.0007758565,0.0004963867,0.0001781433,0.00008031061,0.0002719945,0.0008826913,0.0008125798,0.00003706078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009932142,"about_ca_system_score_gemma":0.0001965289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008580322,"about_ca_topic_score_gemma":0.00003452812,"domain_scores_codex":[0.9959393,0.00002552431,0.001010867,0.001485589,0.000587136,0.0009516541],"domain_scores_gemma":[0.9977217,0.0004192797,0.0006155487,0.0008493757,0.0001981186,0.0001960251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006479174,0.00002872378,0.00000776397,0.0006745817,0.0002704102,0.00008844833,0.001327088,0.191267,0.0005587936,0.3453219,0.0004353803,0.4600134],"study_design_scores_gemma":[0.0003389899,0.00008179482,0.000005359872,0.001212475,0.00004068175,0.00008694021,0.00001243727,0.9117872,0.00002911322,0.08282589,0.002652549,0.0009265975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001331126,0.0003086753,0.9790688,0.0003941023,0.001572373,0.0008512798,0.00002519142,0.001326333,0.01632018],"genre_scores_gemma":[0.000550559,0.00004402104,0.9353038,0.0002666689,0.001620016,0.00001928406,0.0000635596,0.0002810663,0.06185102],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7205201,"threshold_uncertainty_score":0.9994692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05426117843566925,"score_gpt":0.2869551414834059,"score_spread":0.2326939630477367,"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."}}