{"id":"W3147201471","doi":"10.1002/qute.202000069","title":"Noise Robustness and Experimental Demonstration of a Quantum Generative Adversarial Network for Continuous Distributions","year":2021,"lang":"en","type":"article","venue":"Advanced Quantum Technologies","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Canadian Institute for Advanced Research; University of Toronto","funders":"Lawrence Berkeley National Laboratory; Office of Naval Research; University of Toronto; Canada Excellence Research Chairs, Government of Canada; Government of Ontario","keywords":"Robustness (evolution); Adversarial system; Generative grammar; Computer science; Quantum; Noise (video); Acoustics; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001307181,0.0003232953,0.0003532776,0.0002743456,0.0003878656,0.0004218774,0.0009370315,0.0007594943,0.002740894],"category_scores_gemma":[0.003524834,0.0001893045,0.0002502822,0.0002397344,0.001257535,0.0006890023,0.0008606263,0.001027594,0.0002527112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006073705,"about_ca_system_score_gemma":0.0003450331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001417576,"about_ca_topic_score_gemma":0.0008877995,"domain_scores_codex":[0.9995126,0.000171756,0.0000168952,0.00007959921,0.0001515413,0.00006759389],"domain_scores_gemma":[0.9978497,0.00143254,0.0001439825,0.000301271,0.0001831797,0.00008936309],"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":[0.0005270973,0.0003046796,0.002116072,0.0001558899,0.0001014101,0.000338014,0.0001983439,0.8857488,0.05163123,0.04062404,0.001840743,0.01641376],"study_design_scores_gemma":[0.00002427705,0.0001238612,0.0004501798,0.000009693374,0.000007370942,0.00003254778,0.00001854196,0.9692919,0.02227763,0.007320137,0.0004307082,0.00001311366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8182773,0.0002407779,0.1658715,0.001320086,0.0001714531,0.00008624631,0.0002659605,0.0007334325,0.01303327],"genre_scores_gemma":[0.9899957,0.00002841363,0.008911426,0.00006545194,0.000006518778,0.00002192215,0.00005318789,0.00002769314,0.0008896196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002740894,"threshold_uncertainty_score":0.009169161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01014176749704168,"score_gpt":0.2507777043854726,"score_spread":0.2406359368884309,"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."}}