{"id":"W3088153896","doi":"10.1103/prxquantum.2.020340","title":"Single-Shot Error Correction of Three-Dimensional Homological Product Codes","year":2021,"lang":"en","type":"article","venue":"PRX Quantum","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Engineering and Physical Sciences Research Council; Ministry of Colleges and Universities; QuantERA; Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; European Commission; Government of Canada; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada","keywords":"Error detection and correction; Robustness (evolution); Product (mathematics); Coding theory; Algebra over a field","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001808078,0.0003834458,0.000631239,0.0005914433,0.0008034226,0.001238723,0.001400861,0.001143996,0.002167803],"category_scores_gemma":[0.01599848,0.0002636575,0.0003286661,0.0006154836,0.002480687,0.001886965,0.002629397,0.001487026,0.0004707243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006979929,"about_ca_system_score_gemma":0.00112391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008371295,"about_ca_topic_score_gemma":0.0006985685,"domain_scores_codex":[0.998326,0.0003814101,0.00006846339,0.0002336633,0.0008024049,0.000188178],"domain_scores_gemma":[0.9937391,0.00301652,0.0004151547,0.00175948,0.0008164328,0.0002532485],"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":[0.0005440508,0.00006580055,0.00165358,0.000155525,0.00005907804,0.0003191063,0.0005641434,0.1303464,0.02858964,0.7692513,0.001774716,0.06667662],"study_design_scores_gemma":[0.00002368998,0.0001002189,0.0003588364,0.00002766242,0.00001173799,0.0001956428,0.00007280178,0.6380619,0.02484183,0.3347303,0.001522567,0.00005277111],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2907628,0.0004240333,0.6832873,0.0008539379,0.0002797609,0.00008267018,0.000223538,0.001016148,0.02306987],"genre_scores_gemma":[0.95026,0.00009139482,0.04624797,0.0001034642,0.00003830221,0.00003334402,0.00008782718,0.0000784213,0.003059379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002167803,"threshold_uncertainty_score":0.009562135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04069394506398267,"score_gpt":0.2614316899921308,"score_spread":0.2207377449281481,"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."}}