{"id":"W2798967590","doi":"10.1103/physreva.98.012324","title":"Quantum generative adversarial networks","year":2018,"lang":"en","type":"article","venue":"Physical review. A/Physical review, A","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":503,"is_retracted":false,"has_abstract":true,"ca_institutions":"Xanadu Quantum Technologies (Canada)","funders":"","keywords":"Generative grammar; Ansatz; Computer science; Quantum; Adversarial system; Discriminator; Theoretical computer science; Artificial intelligence; Domain (mathematical analysis); Quantum machine learning; Quantum computer; Mathematics; 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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006416047,0.0006779843,0.001646246,0.0000508052,0.0003802167,0.0001394873,0.001960912,0.00005587967,0.00002731101],"category_scores_gemma":[0.0005459662,0.0004906104,0.0009447114,0.001501657,0.0003395211,0.0003856949,0.0008829829,0.0008138526,0.0008652151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006739876,"about_ca_system_score_gemma":0.0001422295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001680829,"about_ca_topic_score_gemma":0.000001996087,"domain_scores_codex":[0.9954807,0.0006538811,0.0007015403,0.00131317,0.0008931367,0.0009576114],"domain_scores_gemma":[0.9966319,0.0005215821,0.0004232232,0.001586113,0.0003551458,0.0004820363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002928139,0.001801446,0.00003416966,0.004006079,0.00028224,0.00007724945,0.000620458,0.0004962446,0.004569935,0.3466344,0.06292722,0.5785213],"study_design_scores_gemma":[0.0003126072,0.0006425806,0.0001181827,0.007207534,0.0001748826,0.00002047947,0.000001155724,0.8699182,0.001191604,0.0436894,0.07593565,0.0007877854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03658309,0.09963725,0.8460738,0.01003931,0.001851537,0.002246622,0.00001657362,0.0007290357,0.002822769],"genre_scores_gemma":[0.8712338,0.07399298,0.01237204,0.02691941,0.01496264,0.0002677116,0.00003816878,0.0001184003,0.00009483443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8694219,"threshold_uncertainty_score":0.9999127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01383444576192066,"score_gpt":0.3331960477269604,"score_spread":0.3193616019650397,"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."}}