{"id":"W4414270633","doi":"10.1109/tnse.2025.3611273","title":"Channel Estimation for Reconfigurable Intelligent Surface-Aided 6G NOMA Systems: A Quantum Machine Learning Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Canada Excellence Research Chairs, Government of Canada","keywords":"Mean squared error; Convolutional neural network; Channel (broadcasting); Artificial neural network; Quantum; Feature (linguistics); Recurrent neural network; Wireless; Deep learning","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":[0.0004166415,0.0001736811,0.0001935395,0.0002550897,0.0003978788,0.00009913707,0.0002761649,0.00008235315,0.000001019615],"category_scores_gemma":[0.00002881648,0.0001837519,0.00003404972,0.001011132,0.00008290136,0.0003042487,0.000003154864,0.000315569,0.000001834106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001594983,"about_ca_system_score_gemma":0.00003007917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001151239,"about_ca_topic_score_gemma":0.000002412992,"domain_scores_codex":[0.9990259,0.000008751658,0.0002439848,0.000236105,0.0001292198,0.0003560654],"domain_scores_gemma":[0.9994116,0.0001531316,0.0000296131,0.0002792193,0.00007600534,0.00005040802],"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":[0.000005044472,0.000009970016,0.000001145796,0.0001319992,0.00001680575,1.043058e-7,0.00006260198,0.9860291,0.001758862,0.0009457576,0.00002596505,0.01101266],"study_design_scores_gemma":[0.0001277083,0.00003021515,0.000006565412,0.000160201,0.000009870249,0.000003319189,0.0001438823,0.9889493,0.009594635,0.0001048307,0.000700617,0.0001688858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008013288,0.001441288,0.9883093,0.00004436566,0.0006483784,0.0003715339,0.000004583032,0.0009541534,0.0002131634],"genre_scores_gemma":[0.9844656,0.0009266877,0.01431228,0.000007321262,0.00001216421,0.0001739775,0.000002950097,0.00002251364,0.0000765162],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9764523,"threshold_uncertainty_score":0.7493186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0174345165895773,"score_gpt":0.2295099038537741,"score_spread":0.2120753872641968,"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."}}