{"id":"W2268989311","doi":"10.1103/physreva.92.062309","title":"Reducing the overhead for quantum computation when noise is biased","year":2015,"lang":"en","type":"article","venue":"Physical Review A","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Army Research Office; Australian Research Council","keywords":"Quantum computer; Gadget; Qubit; Computer science; Quantum error correction; Noise (video); Computation; MAGIC (telescope); Quantum; Algorithm; Topology (electrical circuits); Physics; Quantum mechanics; Engineering; Electrical engineering; Artificial intelligence","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":[0.0004980381,0.0001458376,0.0002774104,0.00002117082,0.0001334206,0.0001017462,0.0005960132,0.00001497494,9.260322e-7],"category_scores_gemma":[0.0002204401,0.00008891516,0.0001787259,0.000268585,0.00003599504,0.0001236783,0.0001583712,0.0001427394,0.00003702116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000251175,"about_ca_system_score_gemma":0.00008980073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002744143,"about_ca_topic_score_gemma":2.945369e-7,"domain_scores_codex":[0.9987892,0.0001110957,0.0002103149,0.000342867,0.0003014152,0.0002451208],"domain_scores_gemma":[0.9988199,0.0003710883,0.0001262626,0.00042784,0.0001347991,0.000120044],"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":[0.00001247751,0.0003023883,0.00001154802,0.0009195506,0.00004942136,0.000004692776,0.006041313,0.005936868,0.000320398,0.05189724,0.1265468,0.8079574],"study_design_scores_gemma":[0.0002007484,0.0001146146,0.00005042333,0.0005751557,0.00001856429,0.000005768435,0.000004817381,0.9017793,0.0001298917,0.07617123,0.02081881,0.0001307118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02835638,0.009486842,0.9290624,0.03122164,0.0004639905,0.0009345959,0.000006337208,0.0001997814,0.0002680484],"genre_scores_gemma":[0.9055526,0.0003878993,0.07572751,0.01665417,0.001387795,0.0001482805,0.00001306654,0.00004161341,0.00008701576],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8958424,"threshold_uncertainty_score":0.3625855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05346745862319183,"score_gpt":0.3348981548878685,"score_spread":0.2814306962646766,"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."}}