{"id":"W4399420289","doi":"10.1117/12.3013415","title":"Asymptotic compression rate of quantum autoencoders","year":2024,"lang":"en","type":"article","venue":"","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Computer science; Compression (physics); Quantum; Data compression; Data compression ratio; Artificial intelligence; Image compression; Physics; Materials science; Quantum mechanics; Composite material; Image processing","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":[0.002974896,0.0008243565,0.0009010175,0.0009288538,0.0005624134,0.001196487,0.00114797,0.00157626,0.003848886],"category_scores_gemma":[0.02298502,0.0004522835,0.000494271,0.0006493967,0.001974847,0.003531103,0.001756966,0.002204585,0.00059087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001665469,"about_ca_system_score_gemma":0.0009940005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008888067,"about_ca_topic_score_gemma":0.0006512012,"domain_scores_codex":[0.9982918,0.0004856641,0.00006121389,0.0001861854,0.0007566769,0.0002186074],"domain_scores_gemma":[0.9854007,0.01133199,0.0004777886,0.001177693,0.001389995,0.0002217997],"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":[0.0007135561,0.0001519904,0.001882345,0.0004043043,0.00009617594,0.0004678124,0.0002808718,0.4399244,0.03729581,0.4654413,0.003121733,0.05021975],"study_design_scores_gemma":[0.00001184953,0.00004433847,0.0004872265,0.00006491504,0.00001386972,0.0001615127,0.00002632462,0.9424731,0.0120658,0.04402119,0.0006068589,0.00002296134],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3041355,0.004472784,0.6384048,0.002561987,0.0002770241,0.0001009422,0.000369092,0.0009877207,0.04869017],"genre_scores_gemma":[0.9571056,0.001759409,0.0339846,0.0003270947,0.0002026304,0.0001256667,0.0002838248,0.000236969,0.005974028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003848886,"threshold_uncertainty_score":0.01573294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009182864182024185,"score_gpt":0.2426837689089313,"score_spread":0.2335009047269071,"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."}}