{"id":"W4396546455","doi":"10.1103/physreva.109.052405","title":"Boosting coherence-based protocols with correlated catalysts","year":2024,"lang":"en","type":"article","venue":"Physical review. A/Physical review, A","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Coherence (philosophical gambling strategy); Computer science; Fidelity; Boosting (machine learning); Protocol (science); Bipartite graph; Decoding methods; Quantum; Theoretical computer science; Artificial intelligence; Algorithm; Mathematics; Physics; Telecommunications; Statistics; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001096841,0.0004680193,0.0004982862,0.000383298,0.0004375154,0.0008934853,0.001021944,0.0008879628,0.002501117],"category_scores_gemma":[0.003290875,0.000236728,0.0002909337,0.000512517,0.001530244,0.00144428,0.001921077,0.001244692,0.0003780163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005036891,"about_ca_system_score_gemma":0.0005613027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002451658,"about_ca_topic_score_gemma":0.0002382496,"domain_scores_codex":[0.9993511,0.0002411422,0.00002526051,0.0001026525,0.0001973062,0.00008255405],"domain_scores_gemma":[0.9984414,0.0008649821,0.0001750028,0.0003109665,0.0001257412,0.00008197335],"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":[0.0002487826,0.0002609851,0.0008395563,0.000402734,0.00007066703,0.0002490432,0.000279565,0.09204321,0.09781764,0.7719998,0.00116001,0.03462804],"study_design_scores_gemma":[0.0001223279,0.0004260847,0.0003819297,0.00004367745,0.00004499478,0.0001818194,0.00006434402,0.750443,0.1158879,0.1271004,0.005234603,0.0000688093],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3367614,0.001442327,0.637895,0.0009719536,0.0002122516,0.0002331319,0.0001054141,0.0003663788,0.02201209],"genre_scores_gemma":[0.9531597,0.00049155,0.04391233,0.0001575534,0.00004156178,0.0001355784,0.00004599298,0.00003481467,0.002020931],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002501117,"threshold_uncertainty_score":0.008367121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01980455440532804,"score_gpt":0.3644648313199466,"score_spread":0.3446602769146186,"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."}}