{"id":"W4386232364","doi":"10.1109/ojcoms.2023.3309268","title":"Quantum Machine Learning for Next-G Wireless Communications: Fundamentals and the Path Ahead","year":2023,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Computer science; Wireless; Quantum machine learning; Software deployment; Perspective (graphical); Quantum; Artificial intelligence; Quantum computer; Computer engineering; Telecommunications; Software engineering","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":["sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.003842679,0.0001328412,0.0002771639,0.00003524407,0.002705204,0.0007871996,0.01277474,0.00004742424,9.32882e-7],"category_scores_gemma":[0.0001382148,0.00007539878,0.0003219368,0.0006338196,0.0006213316,0.0003885899,0.005310528,0.0008074744,0.000003080124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004462397,"about_ca_system_score_gemma":0.000132673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006913906,"about_ca_topic_score_gemma":0.00001668531,"domain_scores_codex":[0.9980484,0.0008809664,0.0004892082,0.0001390409,0.0002310859,0.0002112815],"domain_scores_gemma":[0.9941275,0.002302555,0.0006443491,0.002656302,0.0002078406,0.00006150094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001813252,0.0009185678,0.002841638,0.0001718445,0.001686431,0.000002449723,0.1028531,0.02198818,0.004365478,0.36693,0.05768833,0.4403727],"study_design_scores_gemma":[0.001186501,0.00005479914,0.0004166994,0.0001368929,0.00003083806,0.00005403869,0.0009660852,0.953042,0.00004079302,0.01416327,0.02980261,0.0001054251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1558518,0.02005792,0.2653834,0.552402,0.00191588,0.003474519,0.00004911861,0.0002696402,0.0005957965],"genre_scores_gemma":[0.9133735,0.00646449,0.07868887,0.001094727,0.00006262837,0.00004659272,0.000004968352,0.00001974049,0.000244442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9310539,"threshold_uncertainty_score":0.9985932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06784361100376576,"score_gpt":0.3291254766153012,"score_spread":0.2612818656115354,"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."}}