{"id":"W3038572776","doi":"10.22331/q-2020-11-17-364","title":"Operational, gauge-free quantum tomography","year":2020,"lang":"en","type":"article","venue":"Quantum","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; TRIUMF","funders":"Pacific Northwest National Laboratory; National Nuclear Security Administration; Office of Science; Government of Canada; Advanced Scientific Computing Research; Sandia National Laboratories; Laboratory Directed Research and Development; U.S. Department of Energy","keywords":"Tomography; Computer science; Benchmarking; Set (abstract data type); Parameterized complexity; Ambiguity; Representation (politics); Gauge (firearms); Observable; Algorithm; Process (computing); Computer engineering; Theoretical computer science; Physics; Optics","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":[0.001671315,0.0006151572,0.0006487193,0.0004851061,0.0004706292,0.002108147,0.001796665,0.00129435,0.004258566],"category_scores_gemma":[0.004848842,0.0003769872,0.0005223767,0.0005175809,0.002639072,0.003524384,0.002262505,0.002157111,0.0009989534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001146992,"about_ca_system_score_gemma":0.001666057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001753045,"about_ca_topic_score_gemma":0.001852172,"domain_scores_codex":[0.9988159,0.0004010173,0.00005272,0.0001769992,0.0004254211,0.0001279382],"domain_scores_gemma":[0.9988294,0.0004518367,0.00008937673,0.0004319873,0.0001252727,0.00007200975],"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":[0.0001034006,0.00005630332,0.0004468499,0.00005999536,0.00001464962,0.00007341489,0.0001364047,0.04880365,0.008662182,0.9095737,0.001836938,0.03023255],"study_design_scores_gemma":[0.00003086089,0.00005052416,0.0002037388,0.00002830739,0.000010169,0.00006973949,0.00005105034,0.6018553,0.00883479,0.3832484,0.005586123,0.00003103378],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01083295,0.00009096132,0.9784521,0.0003656548,0.00003743069,0.00004958326,0.000152689,0.0007994762,0.009219122],"genre_scores_gemma":[0.5429214,0.0003032999,0.4510544,0.0002947593,0.00006389189,0.0001736583,0.0003276969,0.0003104897,0.004550442],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004258566,"threshold_uncertainty_score":0.01424634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01639338014628364,"score_gpt":0.2289598460008861,"score_spread":0.2125664658546025,"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."}}