{"id":"W4399922836","doi":"10.1038/s41534-024-00856-3","title":"A benchmarking study of quantum algorithms for combinatorial optimization","year":2024,"lang":"en","type":"article","venue":"npj Quantum Information","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; QLT (Canada)","funders":"Office of International Science and Engineering; Directorate for Computer and Information Science and Engineering; Innovation, Science and Economic Development Canada","keywords":"Benchmarking; Quantum computer; Algorithm; Computer science; Quantum algorithm; Quantum; Combinatorial optimization; Theoretical computer science; Quantum mechanics; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0007072724,0.000197213,0.0002423847,0.0003307618,0.0002101039,0.0004467014,0.0005257165,0.00009266605,0.000004928444],"category_scores_gemma":[0.00007213081,0.0001761731,0.0001065693,0.000751295,0.00002385748,0.001722066,0.0001560431,0.0001794624,0.00000925353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005051067,"about_ca_system_score_gemma":0.0001149165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004207663,"about_ca_topic_score_gemma":7.550067e-7,"domain_scores_codex":[0.9982477,0.00006087574,0.0006825437,0.0002616515,0.0004628256,0.0002844413],"domain_scores_gemma":[0.9988728,0.0002380743,0.0002207426,0.0003674403,0.0002369235,0.0000640198],"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":[0.0000515533,0.0002961568,0.00004639643,0.0003408738,0.00009083571,0.000005711684,0.01577174,0.5330541,0.000027853,0.2608132,0.0009262281,0.1885753],"study_design_scores_gemma":[0.0007890795,0.001046934,0.0001199959,0.0000964584,0.00001767066,0.00001532756,0.0002831907,0.9896275,0.0000492751,0.005360995,0.002389066,0.0002045035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05387473,0.00008068851,0.9404579,0.0001982339,0.004162691,0.0007478003,0.00001097242,0.000367636,0.00009930739],"genre_scores_gemma":[0.9521909,0.0000117682,0.04731758,0.0000467734,0.0003048529,0.00006016258,0.00005124544,0.00001337374,0.000003375721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8983161,"threshold_uncertainty_score":0.7184131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01356478418964921,"score_gpt":0.2604990442905223,"score_spread":0.2469342601008731,"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."}}