{"id":"W2612640646","doi":"10.1103/physrevlett.120.160503","title":"Quantum and Private Capacities of Low-Noise Channels","year":2018,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Classical capacity; Physics; Quantum capacity; Quantum channel; Amplitude damping channel; Quantum; Superadditivity; Channel (broadcasting); Noise (video); Quantum noise; Quantum mechanics; Degenerate energy levels; Statistical physics; Computer science; Telecommunications; Topology (electrical circuits); Quantum information; Quantum network; Mathematics; Electrical engineering; Engineering; Mathematical economics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001559222,0.0001402706,0.0003198675,0.00003422975,0.00007016277,0.00003229945,0.000421145,0.00000664846,0.000002519577],"category_scores_gemma":[0.00003700993,0.0001051847,0.00008858079,0.0002273228,0.000315739,0.0001161202,0.0002127369,0.0001129193,0.00001896286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005960363,"about_ca_system_score_gemma":0.000009480652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005240142,"about_ca_topic_score_gemma":8.999584e-8,"domain_scores_codex":[0.9990423,0.00007038084,0.0001834331,0.0002805837,0.0001977175,0.0002256244],"domain_scores_gemma":[0.9993169,0.00009419164,0.0001059303,0.0003698733,0.00003845366,0.00007466666],"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":[0.00003314617,0.0009214569,0.0004365851,0.02069438,0.0003588268,0.00009554504,0.02558223,0.00161415,0.2548011,0.3354649,0.02520791,0.3347897],"study_design_scores_gemma":[0.0003857424,0.0004313992,0.001400498,0.006366547,0.00004102907,0.00003764977,0.00000920309,0.9565348,0.01211997,0.012745,0.009242967,0.0006851786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.931725,0.002258432,0.05907391,0.006421195,0.0002434936,0.0001641801,0.000001985139,0.00007085651,0.00004091479],"genre_scores_gemma":[0.9873043,0.0004946779,0.003570167,0.008239133,0.0003728462,0.000006384731,6.020869e-7,0.000008587859,0.00000332739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9549206,"threshold_uncertainty_score":0.4289307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01286078247914865,"score_gpt":0.2563534226513246,"score_spread":0.243492640172176,"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."}}