{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007975895,0.0006029747,0.0007861961,0.0004587714,0.0007429906,0.001268782,0.001193662,0.001020148,0.002746454],"category_scores_gemma":[0.005896564,0.0002075381,0.0003977881,0.00042272,0.001544159,0.002965529,0.001079207,0.001000176,0.0004634223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000953629,"about_ca_system_score_gemma":0.0007338536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003882356,"about_ca_topic_score_gemma":0.0003789819,"domain_scores_codex":[0.9992858,0.0001653809,0.00002886876,0.00009092561,0.0002739303,0.0001551244],"domain_scores_gemma":[0.9973174,0.00143823,0.0002757978,0.0006830522,0.0001868814,0.00009860779],"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":[0.0007104139,0.0002332578,0.001910483,0.0002439613,0.00005606259,0.0004588829,0.0002323778,0.3156862,0.1335299,0.5232092,0.000962137,0.02276722],"study_design_scores_gemma":[0.00003737467,0.0001573636,0.0003506075,0.00002228477,0.00003171064,0.0001519712,0.00004148101,0.8730044,0.04017386,0.08482938,0.00116733,0.0000323652],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6273438,0.0006083962,0.3540498,0.001372249,0.0001407679,0.0001168695,0.000121224,0.0006779497,0.01556897],"genre_scores_gemma":[0.9807615,0.000153776,0.01770457,0.00006811141,0.00002655213,0.00004322477,0.00002447691,0.00006767217,0.001150206],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002746454,"threshold_uncertainty_score":0.009187818,"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."}}