{"id":"W2148118219","doi":"10.1088/1464-4266/7/10/021","title":"Scalable noise estimation with random unitary operators","year":2005,"lang":"en","type":"article","venue":"Journal of Optics B Quantum and Semiclassical Optics","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":516,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute","funders":"","keywords":"Fidelity; Scalability; Noise (video); Computer science; Unitary state; Quantum tomography; Algorithm; Unitary transformation; Quantum; Mathematics; Theoretical computer science; Quantum state; Quantum mechanics; Artificial intelligence; Physics; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0005138053,0.0001902997,0.0003384915,0.0002576302,0.0001572254,0.0003283978,0.0003914111,0.00009963221,0.000006184869],"category_scores_gemma":[0.0001349367,0.0001319785,0.00009819029,0.0004535989,0.0001409591,0.001643434,0.00007583525,0.0003758658,0.00001228313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003457591,"about_ca_system_score_gemma":0.0001473012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.422865e-7,"about_ca_topic_score_gemma":8.219413e-7,"domain_scores_codex":[0.9983543,0.00005160405,0.000678861,0.0001369885,0.0005135371,0.0002647068],"domain_scores_gemma":[0.9982775,0.0001837157,0.00035262,0.0002339052,0.0006353337,0.0003168871],"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":[0.0005913749,0.0007873903,0.001230594,0.0001193253,0.0002777576,0.000109037,0.003002351,0.07453904,0.001768046,0.8733531,0.00557677,0.03864526],"study_design_scores_gemma":[0.002224072,0.000575079,0.0002774926,0.00008223756,0.00004402299,0.0002891702,0.0001709098,0.9900642,0.001052583,0.002095831,0.002908706,0.0002156808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4178622,0.0002210778,0.5783579,0.002248295,0.0002347881,0.00009021754,0.000001881825,0.00003570923,0.0009478871],"genre_scores_gemma":[0.7635352,0.0003798628,0.2352923,0.000585299,0.0001605423,0.000001061402,0.000001573395,0.00001074981,0.00003342043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9155252,"threshold_uncertainty_score":0.5381926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00849356519664655,"score_gpt":0.2245449501597384,"score_spread":0.2160513849630918,"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."}}