{"id":"W4410431002","doi":"10.1016/j.mtquan.2025.100042","title":"More buck-per-shot: Why learning trumps mitigation in noisy quantum sensing","year":2025,"lang":"en","type":"article","venue":"Materials Today Quantum","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Schwartz/Reisman Emergency Medicine Institute; University of Waterloo","funders":"Los Alamos National Laboratory; Office of Science; Rhodes Scholarships; Advanced Scientific Computing Research; Engineering and Physical Sciences Research Council; Laboratory Directed Research and Development; U.S. Department of Energy","keywords":"Shot (pellet); Quantum; Computer science; Artificial intelligence; Physics; Materials science; Quantum mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001041384,0.0003244984,0.0004708431,0.0007647108,0.0002948887,0.0007050733,0.0006324932,0.0001956266,0.0001125604],"category_scores_gemma":[0.0001349807,0.0003193204,0.0001140093,0.001066656,0.00009732195,0.001068333,0.0002337225,0.0002601127,0.0001722605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007991972,"about_ca_system_score_gemma":0.0001090319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002235907,"about_ca_topic_score_gemma":0.00002322292,"domain_scores_codex":[0.9972816,0.0003119073,0.0009285478,0.0004983753,0.0003971556,0.0005824097],"domain_scores_gemma":[0.998786,0.0001214314,0.0002791859,0.0005802065,0.0001343734,0.00009881813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009593751,0.0001310378,0.001774998,0.0003419911,0.00005201907,0.00004788766,0.006030048,0.0007659393,0.2541477,0.7253531,0.004178223,0.00708119],"study_design_scores_gemma":[0.003494793,0.0003347542,0.02822161,0.00125418,0.00004731654,0.0000791564,0.002909925,0.3937066,0.4113859,0.09217906,0.064379,0.002007654],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8829134,0.00009292592,0.108267,0.003957408,0.002184085,0.0003373905,0.000007156108,0.0004755356,0.001765025],"genre_scores_gemma":[0.9951752,0.00004336379,0.002362594,0.002167791,0.00007085917,0.00001972595,0.00004373675,0.00001935404,0.00009730974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.633174,"threshold_uncertainty_score":0.9999259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01052565675827631,"score_gpt":0.251467193931027,"score_spread":0.2409415371727506,"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."}}