{"id":"W2165985653","doi":"10.26421/qic15.1-2-10","title":"Efficient Clifford+T approximation of single-qubit operators","year":2015,"lang":"en","type":"article","venue":"Quantum Information and Computation","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Mathematical Sciences; Natural Sciences and Engineering Research Council of Canada; Intelligence Advanced Research Projects Activity","keywords":"Mathematics; Combinatorics; Sequence (biology); Binary logarithm; Operator (biology); Constant (computer programming); Product (mathematics); Log-log plot; Polynomial; Upper and lower bounds; Discrete mathematics; Mathematical analysis; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004243736,0.0001200807,0.0001511197,0.00022326,0.00009572604,0.0001818148,0.0001882951,0.00005264465,7.796885e-7],"category_scores_gemma":[0.00006028074,0.000106095,0.0000338366,0.0004068672,0.00004163723,0.0005299285,0.0001033852,0.0000798063,0.00001800663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003130754,"about_ca_system_score_gemma":0.00007033011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001537925,"about_ca_topic_score_gemma":3.743505e-7,"domain_scores_codex":[0.9988554,0.00005622913,0.0004632822,0.0001324848,0.0003440783,0.0001484889],"domain_scores_gemma":[0.9991409,0.00004836385,0.0002684244,0.0001491028,0.0002834511,0.0001097104],"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":[0.00001715304,0.0000871109,0.00009908888,0.00007129645,0.00001086823,5.945774e-7,0.008878926,0.5611047,0.0001478659,0.1006471,0.0002598991,0.3286754],"study_design_scores_gemma":[0.0005356914,0.0002061432,0.0005115544,0.00002643092,0.000003150603,0.00001915231,0.0002979499,0.993515,0.000390561,0.003868715,0.0004982217,0.0001274429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3891773,0.00003094683,0.6097142,0.0002167706,0.0002211578,0.0001207894,0.000001607565,0.0000972526,0.000420042],"genre_scores_gemma":[0.9603011,0.000001443999,0.03947201,0.0001672696,0.00002524132,0.000004416799,0.00002226238,0.000003660933,0.000002597855],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5711238,"threshold_uncertainty_score":0.4326427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0183306586862418,"score_gpt":0.2417000462960589,"score_spread":0.2233693876098171,"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."}}