{"id":"W4403884712","doi":"10.48550/arxiv.2410.03628","title":"Supporting Data for PRX Quantum (Universal Adapters between quantum LDPC codes)","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Simons Institute for the Theory of Computing, University of California Berkeley; Innovation, Science and Economic Development Canada; Institut Périmètre de physique théorique; Government of Canada; Ministry of Colleges and Universities; U.S. Department of Energy","keywords":"Low-density parity-check code; Computer science; Quantum; Error floor; Theoretical computer science; Physics; Algorithm; Decoding methods; Quantum mechanics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001044871,0.0006811732,0.0007932169,0.0005609632,0.0004276144,0.0004969793,0.007029431,0.0004559815,0.00001110197],"category_scores_gemma":[0.0001026236,0.0007587672,0.0004265621,0.000838978,0.000191581,0.0004415211,0.01428885,0.001513015,0.00007713748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001999581,"about_ca_system_score_gemma":0.0006501462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002967406,"about_ca_topic_score_gemma":0.0000399436,"domain_scores_codex":[0.9950288,0.0001803623,0.0004931511,0.003027227,0.0002323706,0.001038123],"domain_scores_gemma":[0.9948388,0.0005499314,0.0005369097,0.003523689,0.0001804412,0.0003702484],"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":[0.0000825379,0.0001746382,0.0008889536,0.001319206,0.001118278,0.001740656,0.001578418,0.345918,0.00005947948,0.6203472,0.01128443,0.0154882],"study_design_scores_gemma":[0.0004092272,0.0001232844,0.000140772,0.0003100099,0.0002337197,0.0000118529,0.0001469081,0.8713838,0.00001541932,0.1224924,0.003956622,0.00077589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2026555,0.0001550519,0.792049,0.0007877631,0.001848401,0.0006442164,0.000757425,0.0008964645,0.0002061461],"genre_scores_gemma":[0.9861334,0.00003178157,0.01200408,0.00009053349,0.0005742905,0.000001430297,0.0004596565,0.00007489565,0.0006299467],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7834778,"threshold_uncertainty_score":0.9994863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09568873070373073,"score_gpt":0.2430315538123722,"score_spread":0.1473428231086415,"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."}}