{"id":"W4380685397","doi":"10.1049/qtc2.12061","title":"User trajectory prediction in mobile wireless networks using quantum reservoir computing","year":2023,"lang":"en","type":"article","venue":"IET Quantum Communication","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thales (Canada); Université de Sherbrooke; Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reservoir computing; Wireless; Dynamical systems theory; Trajectory; Quantum; Wireless network; Recurrent neural network; Quantum computer; Artificial neural network; Artificial intelligence; Theoretical computer science; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001726755,0.00026135,0.0003393952,0.0003628115,0.0006308189,0.0002747985,0.002152589,0.0001987409,0.000004075206],"category_scores_gemma":[0.00003661691,0.0002648206,0.0001189364,0.002464441,0.00009432528,0.0007609039,0.001412226,0.0007684787,0.00002856247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001616488,"about_ca_system_score_gemma":0.00007146011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002651513,"about_ca_topic_score_gemma":0.0001236652,"domain_scores_codex":[0.9968241,0.0007764284,0.0007420193,0.0005625682,0.0004057812,0.0006890438],"domain_scores_gemma":[0.9970515,0.0006557426,0.0003046654,0.001747686,0.0001298474,0.0001105743],"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":[0.00001315648,0.00008560998,0.007210112,0.00002921326,0.0000142797,0.00001493761,0.0008291262,0.9782149,0.000547927,0.004916016,0.001392996,0.0067317],"study_design_scores_gemma":[0.0003841888,0.0000691765,0.01296296,0.0003082409,0.000005036311,0.00001424589,0.0002117424,0.983681,0.00006351399,0.001115816,0.0009240313,0.0002600489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8747275,0.001006501,0.1218004,0.0004147859,0.0007459343,0.0004460171,0.000001854163,0.0007626112,0.00009439149],"genre_scores_gemma":[0.9958746,0.0005781308,0.003106052,0.00009107145,0.0001968407,0.00002904922,0.00004691346,0.00003856006,0.00003874295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1211471,"threshold_uncertainty_score":0.9999804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03647336064647776,"score_gpt":0.2899972702251959,"score_spread":0.2535239095787181,"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."}}