{"id":"W4234951113","doi":"10.32920/ryerson.14653518","title":"Learning programs for the quantum computer","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"GRASP; Genetic programming; Computer science; Quantum computer; Quantum; Evolutionary programming; Task (project management); Theoretical computer science; Quantum algorithm; Genetic algorithm; Mathematical optimization; Evolutionary algorithm; Artificial intelligence; Mathematics; Programming language; Machine learning; Quantum mechanics; Physics; Engineering","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":[0.0009766909,0.0004602241,0.0004071186,0.0006524393,0.0007078056,0.001520497,0.0006778309,0.0009340655,0.008632548],"category_scores_gemma":[0.00696066,0.000277285,0.0005155057,0.0006661247,0.002058507,0.003011016,0.001523576,0.003343924,0.001284757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001129898,"about_ca_system_score_gemma":0.0009734097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001344296,"about_ca_topic_score_gemma":0.001502162,"domain_scores_codex":[0.9995331,0.0002125077,0.00001847251,0.00008057817,0.0001305113,0.00002494354],"domain_scores_gemma":[0.9985482,0.001082573,0.00005567964,0.0001398329,0.0001237461,0.00004980259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001079347,0.00001833809,0.000166326,0.0000713789,0.000008384467,0.00001764782,0.0001375355,0.02508785,0.000507806,0.9279129,0.003480023,0.04258095],"study_design_scores_gemma":[0.000005277785,0.000007196691,0.00004797719,0.00003138871,0.000002730952,0.00001159156,0.00001570352,0.0864031,0.0002431851,0.902334,0.01089292,0.00000486441],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007667079,0.001376868,0.9682165,0.003856106,0.0001238578,0.00004367224,0.00008144021,0.0003609385,0.01827353],"genre_scores_gemma":[0.240623,0.003641639,0.7340136,0.0008093425,0.0005414416,0.0004005306,0.0002956097,0.0004877105,0.01918712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008632548,"threshold_uncertainty_score":0.02887875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02155592161544562,"score_gpt":0.2562856092082152,"score_spread":0.2347296875927696,"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."}}