{"id":"W2418943682","doi":"10.1103/physrevlett.117.090502","title":"Optimal Compression for Identically Prepared Qubit States","year":2016,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"University of Hong Kong; National Natural Science Foundation of China; National Research Foundation Singapore; Ministry of Education - Singapore; National Research Foundation; Ministry of Education, Culture, Sports, Science and Technology; Foundational Questions Institute; Centre for Quantum Technologies; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Qubit; Independent and identically distributed random variables; Computer science; Quantum; Cloning (programming); Quantum mechanics; Quantum information; Compression (physics); Physics; Statistical physics; Topology (electrical circuits); Mathematics; Combinatorics; Statistics; Thermodynamics; Random variable","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":[0.001796738,0.0003380895,0.0005548878,0.0007539521,0.0007891192,0.001534095,0.001013208,0.0009447496,0.002985673],"category_scores_gemma":[0.01177406,0.0003107272,0.0002755582,0.0003961071,0.003668052,0.003420485,0.002519078,0.001640348,0.0003203597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008296374,"about_ca_system_score_gemma":0.0006292048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002497268,"about_ca_topic_score_gemma":0.000238214,"domain_scores_codex":[0.9985754,0.0003839824,0.0001037311,0.0002002052,0.0004840559,0.0002526898],"domain_scores_gemma":[0.9941941,0.003911884,0.0005394418,0.0006900817,0.0003419258,0.000322532],"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":[0.0001926842,0.00005527426,0.0004710098,0.00007762429,0.00001245623,0.000185073,0.0002147748,0.03403443,0.01528786,0.9400391,0.0003879621,0.00904175],"study_design_scores_gemma":[0.00003131766,0.0001696602,0.000496732,0.00006154761,0.0000138855,0.0001791446,0.0001165129,0.2631819,0.0491142,0.6852683,0.001306163,0.000060583],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6417216,0.001163332,0.3022034,0.001323772,0.0001838697,0.0000827158,0.0002223033,0.0004079121,0.05269111],"genre_scores_gemma":[0.983713,0.0002204077,0.01420479,0.0001298797,0.00004382267,0.00006911305,0.00006087472,0.00003865664,0.001519487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002985673,"threshold_uncertainty_score":0.009988129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01154969920568854,"score_gpt":0.2883482264926293,"score_spread":0.2767985272869408,"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."}}