{"id":"W2772134378","doi":"10.1088/1367-2630/aaccae","title":"Efficient code for relativistic quantum summoning","year":2018,"lang":"en","type":"article","venue":"New Journal of Physics","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Division of Physics; Alberta Innovates - Technology Futures; Gordon and Betty Moore Foundation","keywords":"Quantum; Encoding (memory); Code (set theory); Protocol (science); Scaling; Decoding methods; Point (geometry); Task (project management); Quantum information","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":[0.001332533,0.000429788,0.0006453075,0.000636499,0.001261324,0.001336643,0.001364856,0.001119448,0.005004742],"category_scores_gemma":[0.004962038,0.0002782166,0.0004760453,0.000738708,0.002202303,0.002005934,0.003226104,0.001785429,0.0008761253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00123478,"about_ca_system_score_gemma":0.002426706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00114651,"about_ca_topic_score_gemma":0.001529539,"domain_scores_codex":[0.9986368,0.000274336,0.0001188893,0.0001812059,0.0005570659,0.0002317075],"domain_scores_gemma":[0.9977442,0.000842786,0.0001766126,0.0006317662,0.000463751,0.0001408527],"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":[0.00007733064,0.00003303496,0.0001259499,0.00005534584,0.000008910975,0.0001248643,0.000204867,0.02072754,0.00525256,0.9542243,0.002298516,0.01686673],"study_design_scores_gemma":[0.00007551403,0.0001032453,0.000151931,0.00005107016,0.00002812922,0.000156916,0.00007984356,0.3511444,0.02104712,0.6141281,0.01294618,0.00008758335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05172044,0.0002032849,0.9214121,0.0009999799,0.0002345206,0.0001989413,0.000280098,0.0009084985,0.02404217],"genre_scores_gemma":[0.7552969,0.0002282503,0.2287637,0.0005121262,0.0001306513,0.0004310064,0.0003639682,0.0002187198,0.01405462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005004742,"threshold_uncertainty_score":0.01674253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02789839868827266,"score_gpt":0.2777115641431721,"score_spread":0.2498131654548995,"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."}}