{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007409404,0.0005536365,0.001251312,0.001019959,0.003946327,0.006354044,0.002079888,0.006778538,0.010457],"category_scores_gemma":[0.01925877,0.0004057722,0.0008308007,0.0006872964,0.02732867,0.02530958,0.004518452,0.01287129,0.002215661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002914745,"about_ca_system_score_gemma":0.002442781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001296236,"about_ca_topic_score_gemma":0.0009525946,"domain_scores_codex":[0.9965192,0.00132113,0.0001183881,0.0006047604,0.001072072,0.0003643105],"domain_scores_gemma":[0.9889203,0.007412351,0.0004642947,0.001655254,0.001017458,0.0005303695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000006475193,0.000004373291,0.0000287733,0.00002493503,0.000002616439,0.000009571266,0.00007423304,0.00009615607,0.00006323951,0.9953765,0.002349335,0.001963689],"study_design_scores_gemma":[0.000004766417,0.000007505094,0.00003119539,0.00002378953,0.000001910601,0.00001758661,0.00005623922,0.0004175655,0.00008694814,0.9881651,0.01118139,0.000005876709],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0178507,0.01849929,0.2530528,0.5109684,0.005891454,0.00006282535,0.0003297526,0.0004853367,0.1928595],"genre_scores_gemma":[0.8338441,0.01588412,0.04826605,0.062504,0.01105233,0.0002821824,0.0001756483,0.0007786617,0.02721299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.010457,"threshold_uncertainty_score":0.03918517,"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."}}