{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009682097,0.0005305186,0.0006449997,0.0003542105,0.000515472,0.0009861698,0.001028245,0.001245901,0.00563203],"category_scores_gemma":[0.003612752,0.0003371146,0.0005549116,0.0003536193,0.00208702,0.001554798,0.001531485,0.0021832,0.000744352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00108219,"about_ca_system_score_gemma":0.0005959819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00106114,"about_ca_topic_score_gemma":0.001057216,"domain_scores_codex":[0.9994467,0.0002476533,0.00001426665,0.00009590542,0.0001364721,0.00005899519],"domain_scores_gemma":[0.9988204,0.0007833869,0.00007641339,0.0001811925,0.00008923718,0.00004941605],"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":[0.0000364349,0.0000200484,0.0002814917,0.00005266151,0.00002913591,0.00006286202,0.00004859652,0.3025054,0.001701187,0.6783614,0.002579691,0.01432103],"study_design_scores_gemma":[0.000008950075,0.00001265209,0.00009926686,0.00001891831,0.000006525584,0.00002972943,0.000009220329,0.7640837,0.0008025956,0.230773,0.004142078,0.00001329159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02322843,0.0009140102,0.9384129,0.002165309,0.0002308256,0.0000695873,0.0002106067,0.0004289171,0.03433938],"genre_scores_gemma":[0.8555532,0.001473794,0.120572,0.001057406,0.0002106912,0.0002307556,0.0003128304,0.0002135068,0.02037585],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00563203,"threshold_uncertainty_score":0.01884109,"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."}}