{"id":"W4411503728","doi":"10.22399/ijcesen.2484","title":"Exploring the Synergy Between Neuro-Inspired Algorithms and Quantum Computing in Machine Learning","year":2025,"lang":"en","type":"article","venue":"International Journal of Computational and Experimental Science and Engineering","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Quantum machine learning; Artificial intelligence; Quantum computer; Deep learning; Artificial neural network; Scalability; Quantum; Machine learning; Algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.0005827557,0.0001038321,0.0001311609,0.0003590593,0.0001995753,0.0002386446,0.0004200696,0.00001283441,2.30043e-7],"category_scores_gemma":[0.00007863228,0.00007817633,0.00002218454,0.000306097,0.0001016706,0.0005398759,0.0003878801,0.0002216881,1.070852e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004457191,"about_ca_system_score_gemma":0.00005261663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003421877,"about_ca_topic_score_gemma":4.109545e-7,"domain_scores_codex":[0.9989442,0.00002303263,0.0002732933,0.0001847845,0.0004197811,0.0001548918],"domain_scores_gemma":[0.9993889,0.000310309,0.00008363204,0.00003968706,0.0001094434,0.00006806249],"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":[0.00001656837,0.00004667862,0.007373922,0.00001417023,0.0000585028,0.00005337417,0.003557174,0.7542306,0.005993489,0.02183991,0.000006189829,0.2068094],"study_design_scores_gemma":[0.000321827,0.0000566423,0.05646259,0.00009823205,0.000001568146,0.0001318988,0.0001237978,0.9407327,0.0008802934,0.0008602655,0.0002514641,0.0000786876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9031187,0.0006956235,0.09396138,0.001683892,0.0004751256,0.0000314007,4.873324e-7,0.00001698823,0.00001637776],"genre_scores_gemma":[0.9900851,0.00004920932,0.00962566,0.0001387625,0.00009503451,0.000001016032,4.227272e-7,0.000003210117,0.000001608637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2067308,"threshold_uncertainty_score":0.3187938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01633679222181163,"score_gpt":0.2584388824207557,"score_spread":0.2421020901989441,"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."}}