{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001328938,0.0004569163,0.0006161439,0.0003849875,0.000386156,0.0009548977,0.001581854,0.001118948,0.00385868],"category_scores_gemma":[0.002441905,0.0004374038,0.0005238221,0.0003883579,0.002094697,0.001420195,0.001628897,0.001734012,0.0004384577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001131962,"about_ca_system_score_gemma":0.0008446834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00150582,"about_ca_topic_score_gemma":0.001781724,"domain_scores_codex":[0.9995404,0.0002250156,0.00001164975,0.00005910277,0.0001200197,0.00004377617],"domain_scores_gemma":[0.999234,0.0004994645,0.0000521955,0.0001052139,0.00005913053,0.0000501529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002733235,0.00002316341,0.0001535696,0.00002018673,0.00002054068,0.00004341538,0.00003373076,0.6511711,0.001377507,0.3379341,0.0007856555,0.008409679],"study_design_scores_gemma":[0.000002802116,0.000004647054,0.00001095391,0.000001997118,9.441467e-7,0.000004657058,0.000001551569,0.9635631,0.0001875121,0.03594686,0.0002726019,0.000002420192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00920279,0.0000909645,0.9873842,0.000289359,0.00002557012,0.00002613746,0.00003313067,0.0001324983,0.002815415],"genre_scores_gemma":[0.7907847,0.0002542703,0.1992152,0.0003599967,0.00007624515,0.0002018846,0.0001221259,0.000201261,0.008784217],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00385868,"threshold_uncertainty_score":0.01290858,"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."}}