{"id":"W2907512006","doi":"10.1002/qute.202000003","title":"Variational Quantum Generators: Generative Adversarial Quantum Machine Learning for Continuous Distributions","year":2020,"lang":"en","type":"preprint","venue":"Advanced Quantum Technologies","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto; Canadian Institute for Advanced Research","funders":"Army Research Office; Office of Naval Research","keywords":"Quantum circuit; Quantum machine learning; Computer science; Quantum; Quantum algorithm; Quantum network; Generator (circuit theory); Quantum state; Topology (electrical circuits); Quantum computer; Algorithm; Theoretical computer science; Mathematics; Statistical physics; Quantum mechanics; 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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0005099324,0.001163795,0.00140297,0.0004535646,0.001214144,0.0006011164,0.003464113,0.0009824958,0.000005093033],"category_scores_gemma":[0.002326987,0.00110238,0.0006315343,0.0009361957,0.000384015,0.0004050626,0.00434725,0.002940958,0.0000242837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002776663,"about_ca_system_score_gemma":0.000580708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000556657,"about_ca_topic_score_gemma":0.00001150487,"domain_scores_codex":[0.994163,0.0002528464,0.001180415,0.002467062,0.0007131443,0.001223502],"domain_scores_gemma":[0.9957114,0.0008235949,0.001164162,0.001548729,0.0005720319,0.0001801187],"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":[0.00009446459,0.000228591,0.00008509213,0.0001788016,0.0003291209,0.00006499981,0.0004840817,0.1669012,0.003013596,0.7856177,0.001169013,0.04183331],"study_design_scores_gemma":[0.0009343415,0.0004634685,0.00004060058,0.0001242783,0.0000496678,0.00001930015,0.0001342943,0.6511588,0.003078039,0.3342792,0.008842921,0.0008750061],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01268429,0.003224195,0.9586886,0.01309758,0.003193091,0.001502482,0.0008918105,0.006696501,0.00002147925],"genre_scores_gemma":[0.6212617,0.0003919007,0.3757442,0.0001717486,0.0005846237,0.000632687,0.001088139,0.0001005279,0.0000244813],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6085774,"threshold_uncertainty_score":0.9993593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01707116633921113,"score_gpt":0.2599485468981357,"score_spread":0.2428773805589245,"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."}}