{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001213117,0.0007380347,0.0008373564,0.0004412728,0.0007719274,0.0008238016,0.004983207,0.0002943001,0.0001442841],"category_scores_gemma":[0.0001500603,0.0007221602,0.000481579,0.0005815155,0.0001100178,0.0001896295,0.01085449,0.002885804,0.00009055025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001754753,"about_ca_system_score_gemma":0.0004145284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000414881,"about_ca_topic_score_gemma":0.00000629827,"domain_scores_codex":[0.994864,0.0004599539,0.0007668262,0.001808326,0.00110966,0.0009912663],"domain_scores_gemma":[0.9961963,0.0003818823,0.0005726018,0.002455865,0.0001196393,0.0002736775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000347248,0.0006237598,0.0005839111,0.000863368,0.0004063611,0.0009812076,0.00821296,0.4200336,0.0003438329,0.4953281,0.02116493,0.05142325],"study_design_scores_gemma":[0.000195001,0.0001137526,0.0005211217,0.0001401709,0.00002147109,0.0000699903,0.00005571665,0.8435616,0.00006083609,0.1246616,0.02979835,0.0008002951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2410781,0.004193068,0.726881,0.008327257,0.01358565,0.0009919629,0.0001435743,0.003052429,0.001746965],"genre_scores_gemma":[0.925442,0.0003067909,0.07031341,0.001642969,0.001226977,0.0002293768,0.0002017349,0.0001608492,0.0004758495],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.684364,"threshold_uncertainty_score":0.9995229,"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."}}