{"id":"W4318975179","doi":"10.1038/s41467-023-36144-5","title":"Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines","year":2023,"lang":"en","type":"article","venue":"Nature Communications","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia Hospital; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Deutscher Akademischer Austauschdienst","keywords":"Support vector machine; Quantum; Computer science; Artificial intelligence; Theoretical computer science; Physics; Quantum mechanics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003671593,0.0001364701,0.0001915079,0.0002045256,0.0003749549,0.00006241106,0.001704706,0.0001670622,0.000002933245],"category_scores_gemma":[0.0001855711,0.0001261711,0.00008449424,0.0006262413,0.0001458837,0.0001739509,0.0007749809,0.0004136717,0.000003375241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001965804,"about_ca_system_score_gemma":0.0001446882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001807847,"about_ca_topic_score_gemma":0.00001328331,"domain_scores_codex":[0.9989477,0.0001200975,0.0002373328,0.0002793926,0.0002010041,0.000214504],"domain_scores_gemma":[0.9973183,0.001092013,0.0001730322,0.001137282,0.0002053869,0.00007398975],"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":[0.00001288847,0.00007867909,0.001001296,0.00003696178,0.00006353484,0.000002008707,0.001206887,0.001029531,0.001756559,0.9837735,0.005020263,0.006017865],"study_design_scores_gemma":[0.0003720491,0.00007358857,0.02183732,0.00002840844,0.00001405565,0.000007936224,0.00005805143,0.9437269,0.0001274277,0.01751905,0.01607712,0.0001580957],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1714271,0.003026267,0.7731653,0.04471207,0.00298647,0.001551083,0.0007468145,0.001458395,0.0009264285],"genre_scores_gemma":[0.9479633,0.00008115352,0.05153067,0.0001523983,0.00006781884,0.00002709598,0.0000987009,0.00001433368,0.00006451847],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9662545,"threshold_uncertainty_score":0.5145109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02298284112297075,"score_gpt":0.2964032396326507,"score_spread":0.2734203985096799,"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."}}