{"id":"W4393305093","doi":"10.1038/s41467-024-46959-5","title":"Enhancing combinatorial optimization with classical and quantum generative models","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Quantum; Context (archaeology); Quantum computer; Optimization problem; Mathematical optimization; Quantum state; Theoretical computer science; Artificial intelligence; Algorithm; Mathematics","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":[0.001687272,0.0007163313,0.0009809447,0.00049954,0.0004698569,0.001426064,0.001353237,0.00143373,0.002954048],"category_scores_gemma":[0.005312606,0.0004547875,0.0009143528,0.0006518317,0.001725142,0.00227521,0.001830422,0.001849542,0.0003924189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001163995,"about_ca_system_score_gemma":0.001101576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001837904,"about_ca_topic_score_gemma":0.003224064,"domain_scores_codex":[0.9993082,0.0003524078,0.0000230016,0.00009657162,0.0001501038,0.00006972915],"domain_scores_gemma":[0.9980001,0.001410967,0.0001209866,0.0002833328,0.0001031525,0.00008141896],"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":[0.00003276215,0.00005208459,0.0005316595,0.00006387541,0.00003473114,0.00004549133,0.00003781426,0.8354428,0.000840484,0.1458056,0.001137431,0.01597516],"study_design_scores_gemma":[0.000005957684,0.00001011272,0.00003698506,0.000003827366,0.000003282611,0.00000975367,0.000004360738,0.9520322,0.0002025319,0.04728859,0.0003990011,0.000003421693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06459215,0.0006480312,0.9206031,0.001290963,0.00009597564,0.00005908142,0.0001114811,0.0004662871,0.01213304],"genre_scores_gemma":[0.806473,0.0004478237,0.1880319,0.0004834294,0.00009930708,0.0001139982,0.0001845524,0.000197892,0.003968176],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002954048,"threshold_uncertainty_score":0.009882212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01343223045799782,"score_gpt":0.2658933841866807,"score_spread":0.2524611537286829,"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."}}