{"id":"W4388500029","doi":"10.22331/q-2023-11-08-1175","title":"Automated Generation of Shuttling Sequences for a Linear Segmented Ion Trap Quantum Computer","year":2023,"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":"Université de Sherbrooke","funders":"Army Research Office; Bundesministerium für Bildung und Forschung; Office of the Director of National Intelligence; Intelligence Advanced Research Projects Activity; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke","keywords":"Qubit; Toffoli gate; Quantum computer; Computer science; Overhead (engineering); Quantum circuit; Trapped ion quantum computer; Quantum; Quantum gate; Algorithm; Topology (electrical circuits); Physics; Quantum error correction; Quantum mechanics; Electrical engineering; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000636467,0.0002206318,0.0003168521,0.0002987962,0.0002548555,0.0001026765,0.0006240109,0.0001027777,0.000002478394],"category_scores_gemma":[0.00004357218,0.0001926988,0.0001604177,0.0009675259,0.00005929968,0.0001970958,0.0001586769,0.0001382154,0.00002459094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002443437,"about_ca_system_score_gemma":0.00009509233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003376161,"about_ca_topic_score_gemma":0.000003340579,"domain_scores_codex":[0.9980693,0.000106219,0.0004982881,0.0005335865,0.0003438986,0.0004487429],"domain_scores_gemma":[0.9988785,0.0002276196,0.0002222325,0.0004144588,0.0001683549,0.00008884548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005767144,0.0003557452,0.0002779741,0.0005436665,0.0002317363,0.00007202145,0.006076194,0.5760965,0.1925623,0.1096931,0.01861491,0.09541805],"study_design_scores_gemma":[0.000442759,0.0003917753,0.0004081682,0.00008888892,0.000008653495,0.00001345892,0.00002507056,0.9865152,0.009088568,0.002098649,0.0006897679,0.0002290232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4611977,0.00004205663,0.5352544,0.0007809315,0.001168767,0.0002552002,0.00001505039,0.001280378,0.000005590118],"genre_scores_gemma":[0.8971939,0.00001746609,0.1018244,0.0001552163,0.0006357264,0.00002957324,0.00008354855,0.00002792557,0.00003223171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4359963,"threshold_uncertainty_score":0.7858028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04316642080551727,"score_gpt":0.2940677572313932,"score_spread":0.250901336425876,"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."}}