{"id":"W4417088239","doi":"10.48550/arxiv.2505.06165","title":"Optimization of Quantum Error Correcting Code under Temporal Variation of Qubit Quality","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Qubit; Overhead (engineering); Error detection and correction; Quantum error correction; Word error rate; Quantum computer; Code (set theory); USable","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005960092,0.0003132662,0.0002622341,0.0002190802,0.0002703789,0.00040403,0.0005165659,0.0003215766,0.0004072869],"category_scores_gemma":[0.00352009,0.0001264881,0.0001377489,0.0002719596,0.0006146017,0.0005270793,0.0004908541,0.0004105551,0.00009013759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008280531,"about_ca_system_score_gemma":0.0009087048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003429878,"about_ca_topic_score_gemma":0.004792114,"domain_scores_codex":[0.999551,0.00008858671,0.00001570697,0.00008493358,0.000179622,0.00008014416],"domain_scores_gemma":[0.9987351,0.0005462284,0.0001910169,0.0001934952,0.000284269,0.00004988674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001263968,0.00007454958,0.004748452,0.00005004224,0.00003336964,0.00008726482,0.00009548776,0.879913,0.07844659,0.007366869,0.0005476296,0.02851041],"study_design_scores_gemma":[0.000007610426,0.00004827782,0.0006698992,0.000002271595,0.000005925892,0.0000178576,0.00001940222,0.9791343,0.01863807,0.001254843,0.0001942148,0.000007303555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8142942,0.0001541942,0.1819115,0.0002562313,0.00002004883,0.00002815336,0.00006341735,0.0006004595,0.002671748],"genre_scores_gemma":[0.9774849,0.00002757716,0.0220386,0.00001970287,0.000002823899,0.00001293701,0.00002744048,0.00003577016,0.0003502744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003429878,"threshold_uncertainty_score":0.006819844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05710793136913971,"score_gpt":0.3179313357232599,"score_spread":0.2608234043541202,"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."}}