{"id":"W2604172699","doi":"10.22331/q-2018-01-29-47","title":"Randomized benchmarking with gate-dependent noise","year":2018,"lang":"en","type":"article","venue":"Quantum","topic":"Random Matrices and Applications","field":"Mathematics","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Army Research Office; Canada First Research Excellence Fund","keywords":"Benchmarking; Noise (video); Fidelity; Gaussian noise; Gradient noise; Value noise; Exponential growth; Exponential function","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005500891,0.0001214603,0.0003321524,0.00004754744,0.0001555525,0.00004915359,0.0001459178,0.00004124079,0.0003608057],"category_scores_gemma":[0.00008546488,0.00007880882,0.00007889835,0.0001409793,0.0001320706,0.00005853809,0.0000296934,0.00007667383,0.0001883832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001527235,"about_ca_system_score_gemma":0.00002372123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004781496,"about_ca_topic_score_gemma":0.00002820106,"domain_scores_codex":[0.9990852,0.00006374744,0.0002538987,0.0001926897,0.0002067355,0.0001977202],"domain_scores_gemma":[0.9989048,0.0004747114,0.000147781,0.0003228268,0.00008815616,0.00006174522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.02054459,0.0002321116,0.0001433859,0.00008977667,0.0001629369,0.000009961993,0.001161049,0.000005708437,0.0005454497,0.9655371,0.01050152,0.001066439],"study_design_scores_gemma":[0.3405935,0.0003075807,0.0001094153,0.0001755145,0.0005058349,0.00006007102,0.0003931043,0.02736596,0.003107783,0.6093659,0.01727992,0.0007353385],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8873826,0.0001395417,0.07530136,0.000644306,0.0002130732,0.001162291,0.000006901658,0.0002318278,0.03491805],"genre_scores_gemma":[0.9936265,0.00004751795,0.004889864,0.00006928526,0.0003535116,0.0001269252,0.000002960746,0.0000251921,0.0008582386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3561711,"threshold_uncertainty_score":0.3950569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03477509870037494,"score_gpt":0.3124885890434968,"score_spread":0.2777134903431219,"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."}}