{"id":"W4285390635","doi":"10.1103/prxquantum.3.030101","title":"Is Quantum Advantage the Right Goal for Quantum Machine Learning?","year":2022,"lang":"en","type":"article","venue":"PRX Quantum","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":199,"is_retracted":false,"has_abstract":true,"ca_institutions":"Xanadu Quantum Technologies (Canada)","funders":"","keywords":"Computer science; Perspective (graphical); Quantum machine learning; Quantum; Narrative; Focus (optics); Artificial intelligence; Machine learning; Quantum computer; Cognitive science; Psychology; Physics","routes":{"ca_aff":true,"ca_fund":false,"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","sts"],"consensus_categories":[],"category_scores_codex":[0.001660821,0.0005078634,0.0005032184,0.0002319853,0.003025312,0.0003505703,0.003185296,0.00008505159,0.000215055],"category_scores_gemma":[0.0001605662,0.0003709155,0.0004550135,0.0008999193,0.0001507641,0.0002628417,0.001737761,0.001360409,0.00007456575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001015688,"about_ca_system_score_gemma":0.000181514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001073647,"about_ca_topic_score_gemma":0.000008409556,"domain_scores_codex":[0.9956225,0.0005365544,0.0006386176,0.001139796,0.0009462427,0.001116287],"domain_scores_gemma":[0.9970978,0.000903303,0.0004011213,0.001271898,0.0001178285,0.000208085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002126524,0.0007199872,0.001393565,0.0001597783,0.0002383649,0.000186758,0.008387986,0.02101063,0.001350455,0.9018527,0.03606075,0.02842636],"study_design_scores_gemma":[0.0005749598,0.0006031116,0.0002570719,0.00001228328,0.00001852769,0.0001408692,0.000128875,0.668534,0.0001974497,0.03572737,0.293407,0.0003984946],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1922938,0.005021561,0.7476642,0.04174842,0.007647414,0.002386158,0.0003900435,0.002011454,0.0008369792],"genre_scores_gemma":[0.9889994,0.0000495776,0.00563645,0.003104155,0.0004844076,0.0002994218,0.00005686327,0.00009271814,0.001277007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8661253,"threshold_uncertainty_score":0.9998743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01001854372414951,"score_gpt":0.2488840593246064,"score_spread":0.2388655156004569,"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."}}