{"id":"W3217663389","doi":"10.1103/prxquantum.3.030319","title":"Morphing Quantum Codes","year":2022,"lang":"en","type":"preprint","venue":"PRX Quantum","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Government of Canada; Ministry of Colleges and Universities; Innovation, Science and Economic Development Canada; Institut Périmètre de physique théorique; Simons Foundation","keywords":"Computer science; Morphing; Qubit; Code (set theory); Toric code; Algorithm; Reed–Muller code; Quantum; Theoretical computer science; Concatenated error correction code; Quantum computer; Decoding methods; Block code; Physics; Quantum mechanics; Programming language; Artificial intelligence","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":[0.0001991543,0.0002840255,0.0002416338,0.0005025697,0.0004532838,0.0004936777,0.0004771483,0.0004492957,0.004025949],"category_scores_gemma":[0.001317866,0.0001688199,0.0003713849,0.0003413265,0.0008013891,0.0009076188,0.00108323,0.0008311222,0.0005720957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000399608,"about_ca_system_score_gemma":0.0003174156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004281927,"about_ca_topic_score_gemma":0.0004593195,"domain_scores_codex":[0.9997382,0.00003339384,0.00001867768,0.00006133439,0.00009964369,0.00004876908],"domain_scores_gemma":[0.9995252,0.00009858445,0.00005946149,0.0002116275,0.00006766125,0.00003744439],"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":[0.0002379189,0.00008721981,0.00148089,0.0001899794,0.00003414375,0.0002898836,0.000339703,0.05673753,0.1244674,0.6829329,0.003492553,0.1297099],"study_design_scores_gemma":[0.00009426076,0.0003762291,0.001367112,0.00006954071,0.00005345583,0.001023072,0.0001473393,0.38627,0.1775299,0.3700065,0.06293543,0.0001272133],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3474499,0.0004502152,0.6178929,0.0004892162,0.0003225958,0.0002628531,0.0004770554,0.001957397,0.03069777],"genre_scores_gemma":[0.7950003,0.0003056835,0.195096,0.0002368866,0.00006020126,0.0002226586,0.0003128824,0.0003842704,0.00838121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004025949,"threshold_uncertainty_score":0.01346809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02167480132444894,"score_gpt":0.2652634998279234,"score_spread":0.2435886985034745,"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."}}