{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004196247,0.0003103905,0.0005210718,0.000337782,0.0003407146,0.0004921497,0.0006127502,0.0004865088,0.0008952557],"category_scores_gemma":[0.001513602,0.0002281836,0.0003037219,0.0004281166,0.0004347964,0.001123778,0.0004905468,0.0006901951,0.00009804928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007167096,"about_ca_system_score_gemma":0.0006371578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00858918,"about_ca_topic_score_gemma":0.004505026,"domain_scores_codex":[0.9998492,0.00003963861,0.00000591259,0.00003122606,0.00004114128,0.00003279881],"domain_scores_gemma":[0.9995027,0.0002934145,0.00004532886,0.00003719618,0.00009188303,0.00002959329],"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":[0.0001037377,0.00005425698,0.003036893,0.00003658402,0.00003364815,0.0001070265,0.00004486678,0.9667273,0.004509515,0.008256267,0.0004367049,0.01665316],"study_design_scores_gemma":[8.567621e-7,0.000003849828,0.00005902941,5.753186e-7,8.956734e-7,0.000001849548,0.0000016292,0.9992466,0.000272238,0.000387641,0.00002360754,0.000001237783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4888999,0.0004598994,0.5051649,0.0005835618,0.000101564,0.00004972807,0.0001564561,0.000497409,0.004086492],"genre_scores_gemma":[0.98877,0.00006914684,0.01064577,0.00002527614,0.0000060398,0.0000118936,0.00003883847,0.000008982367,0.0004241471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00858918,"threshold_uncertainty_score":0.01707834,"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."}}