{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00214963,0.0005807749,0.0005300367,0.001854473,0.00120338,0.002348181,0.00117634,0.0009916268,0.005899931],"category_scores_gemma":[0.01304704,0.0003146533,0.0004117933,0.0008839087,0.005045292,0.006324206,0.002220609,0.002116879,0.0007266835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001465396,"about_ca_system_score_gemma":0.001035096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000703265,"about_ca_topic_score_gemma":0.0006220322,"domain_scores_codex":[0.9986894,0.0003742894,0.00004419058,0.0001647369,0.0004336395,0.0002938868],"domain_scores_gemma":[0.9866274,0.009229703,0.001075706,0.00142087,0.001075292,0.0005710836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006212323,0.00002780417,0.000327967,0.0000753377,0.00001132923,0.0000505831,0.0001564414,0.03640888,0.00357691,0.9543127,0.0006682075,0.004321807],"study_design_scores_gemma":[0.0000120118,0.00002059851,0.000355931,0.00004744321,0.00001063294,0.0001111676,0.00008530879,0.134539,0.007215092,0.8560073,0.00155433,0.00004118165],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6331487,0.001848739,0.2530645,0.002984888,0.0001985102,0.00006785473,0.0007868551,0.0002619543,0.107638],"genre_scores_gemma":[0.9860448,0.000601155,0.009422601,0.000122253,0.0001232964,0.00004790004,0.0001252614,0.00004758914,0.003465187],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005899931,"threshold_uncertainty_score":0.01973724,"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."}}