{"id":"W4320561180","doi":"10.3929/ethz-b-000592781","title":"Tailoring Three-Dimensional Topological Codes for Biased Noise","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; University of Waterloo; Perimeter Institute","funders":"","keywords":"Topology (electrical circuits); Qubit; Toric code; Decoding methods; Lattice (music); Pauli exclusion principle; Physics; Algorithm; Mathematics; Quantum mechanics; Topological order; Combinatorics","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":[0.0001423283,0.0001729818,0.0001748583,0.0002239534,0.0002160235,0.0004787144,0.0002814648,0.0004084379,0.0007306385],"category_scores_gemma":[0.001096794,0.0001005841,0.0001248521,0.0002313796,0.0006327936,0.0003531616,0.0004392611,0.0003126722,0.0001493083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000411242,"about_ca_system_score_gemma":0.0003529906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004867354,"about_ca_topic_score_gemma":0.000918882,"domain_scores_codex":[0.9998944,0.00002040028,0.000006964437,0.00001222458,0.00004572155,0.00002031553],"domain_scores_gemma":[0.9994381,0.0002162932,0.0001229309,0.00009942795,0.00008407287,0.0000390946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002339355,0.00009159616,0.003010284,0.0001427561,0.00002585025,0.0003694273,0.0002110249,0.2320781,0.5487849,0.1931726,0.0006006101,0.02127887],"study_design_scores_gemma":[0.00003426171,0.0001879502,0.0009118825,0.00001494847,0.000009323576,0.0001821623,0.0000760317,0.7417404,0.2240102,0.03080153,0.001979629,0.00005164284],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9559855,0.00007104347,0.04006375,0.0001096353,0.00003109531,0.00001324618,0.00005414701,0.0001534867,0.003518142],"genre_scores_gemma":[0.9919851,0.00004966326,0.007365692,0.00002675932,0.000004225445,0.00001619377,0.00003603636,0.00002228651,0.0004939577],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0007306385,"threshold_uncertainty_score":0.002983809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07420260839066285,"score_gpt":0.2107071639089937,"score_spread":0.1365045555183309,"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."}}