{"id":"W2806601829","doi":"10.1103/physrevlett.122.060601","title":"Quantifying Memory Capacity as a Quantum Thermodynamic Resource","year":2019,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Advanced Thermodynamics and Statistical Mechanics","field":"Physics and Astronomy","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies; National Research Foundation; Ministry of Education - Singapore; National Research Foundation Singapore; Agence Nationale de la Recherche; Foundational Questions Institute; John Templeton Foundation","keywords":"Thermodynamic limit; Heat capacity; Qubit; Helmholtz free energy; Statistical physics; Quantum thermodynamics; Degenerate energy levels; Non-equilibrium thermodynamics; Physics; Quantum; Thermodynamic process; Quantum memory; Thermodynamic system; Computer science; Quantum information; Quantum mechanics; Quantum network; Material properties","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000149254,0.0002666843,0.0005210732,0.00002314625,0.00007970779,0.00002810007,0.0002797466,0.00001010535,0.0004208395],"category_scores_gemma":[0.00001870401,0.000223376,0.0002661466,0.0001713395,0.0000513465,0.0001081538,0.00007917443,0.0003482868,0.000961898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003903252,"about_ca_system_score_gemma":0.00001714199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005516678,"about_ca_topic_score_gemma":5.872218e-7,"domain_scores_codex":[0.9985234,0.000110869,0.0002651503,0.0004203771,0.0002782999,0.0004019366],"domain_scores_gemma":[0.9989937,0.0002187796,0.0001487802,0.0004809353,0.00002888593,0.0001289527],"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":[0.00001658723,0.0001795693,0.0001787996,0.0005680028,0.00009508204,0.00000472227,0.0001186787,0.0003096116,0.06432094,0.9146952,0.0002443495,0.01926848],"study_design_scores_gemma":[0.001507098,0.0002846515,0.0009586249,0.004247837,0.0003946056,0.000007812803,0.0002222225,0.7004959,0.0008726256,0.2689948,0.01977189,0.00224196],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9539169,0.0004507284,0.03649766,0.001342972,0.0001451088,0.0006159059,0.00004953559,0.00005861493,0.006922592],"genre_scores_gemma":[0.9944205,0.00005766505,0.0003382213,0.004865546,0.0001560214,0.00004038204,0.00003417051,0.00004483147,0.00004265669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7001863,"threshold_uncertainty_score":0.9998159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02019821679643911,"score_gpt":0.2928245766066869,"score_spread":0.2726263598102478,"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."}}