{"id":"W4392270687","doi":"10.1103/physrevresearch.6.023247","title":"Flying-cat parity checks for quantum error correction","year":2024,"lang":"en","type":"preprint","venue":"Physical Review Research","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Parity (physics); Computer science; Quantum; Error detection and correction; Arithmetic; Mathematics; Algorithm; Physics; Quantum mechanics","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003374092,0.0004386413,0.0009222353,0.0002322146,0.0005412814,0.0006053118,0.002505783,0.0001663935,0.000007442541],"category_scores_gemma":[0.001190281,0.0003448873,0.0007503347,0.001061008,0.0001620791,0.00007468621,0.006457538,0.00334994,0.0003484612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002064287,"about_ca_system_score_gemma":0.0006835955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001425873,"about_ca_topic_score_gemma":0.000008491153,"domain_scores_codex":[0.9948519,0.0006675036,0.0004970604,0.001670254,0.001334334,0.0009789453],"domain_scores_gemma":[0.995963,0.001332535,0.0001464364,0.00165867,0.000609806,0.0002895936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001074884,0.0004334862,0.000004626017,0.02717109,0.0001207093,0.00002743234,0.0007582397,0.0005627633,0.0002657103,0.04773489,0.07835954,0.8445508],"study_design_scores_gemma":[0.00005867098,0.000134675,0.00003188369,0.006409459,0.00002867186,0.000006055047,0.000004240045,0.7427643,0.0001338284,0.2266486,0.02349431,0.0002852977],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05784061,0.2362343,0.6119682,0.05181798,0.0222127,0.01392185,0.000174214,0.002756517,0.003073698],"genre_scores_gemma":[0.9105726,0.02785156,0.03953623,0.001686251,0.01050614,0.005379209,0.0002374086,0.0003705212,0.00386005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.852732,"threshold_uncertainty_score":0.9999003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1232617182708212,"score_gpt":0.458137338269195,"score_spread":0.3348756199983738,"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."}}