{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003523251,0.0004434112,0.0003944194,0.0002183324,0.0002950235,0.0005981087,0.0006763584,0.0005344799,0.001355482],"category_scores_gemma":[0.0008485068,0.0002117444,0.0003287205,0.0002259002,0.0005043266,0.001060958,0.0004501505,0.0007480772,0.0002248782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007366268,"about_ca_system_score_gemma":0.0006275336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004082969,"about_ca_topic_score_gemma":0.005883179,"domain_scores_codex":[0.9998654,0.0000338831,0.000005726493,0.00003049733,0.00003914983,0.00002537461],"domain_scores_gemma":[0.9997084,0.0001413773,0.00004242386,0.0000273071,0.00006833339,0.00001223513],"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":[0.0001050217,0.00006030394,0.001427872,0.0001079066,0.00004922221,0.0001106047,0.00006950308,0.9086256,0.01075681,0.01403734,0.001323869,0.06332602],"study_design_scores_gemma":[0.000001188729,0.000009077031,0.00007091809,0.000002663097,0.000002940438,0.00000682039,0.00000367416,0.9979049,0.0007430846,0.001064171,0.0001879219,0.000002732034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09368083,0.001078112,0.8964623,0.000801674,0.0001223471,0.00004239339,0.0001359606,0.0008477134,0.006828762],"genre_scores_gemma":[0.9557787,0.0003271121,0.04107511,0.0001597279,0.00003844043,0.00003671031,0.0000809898,0.00003696103,0.002466334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004082969,"threshold_uncertainty_score":0.008118451,"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."}}