{"id":"W7042417706","doi":"","title":"An Optimizing Pulse Sequence Compiler for NMR QIP","year":2006,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Waterloo","keywords":"Quantum computer; Compiler; Quantum; Pulse sequence; Quantum algorithm; Set (abstract data type); Sequence (biology); Quantum information; Quantum phase estimation algorithm","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":{"n_in":0,"stratum":"fund_new","weight":1678.9,"opus":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"high","reason":"Dissertation building a pulse-sequence compiler for NMR quantum computing; domain engineering."},"gpt":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"high","reason":"This dissertation develops a compiler for quantum computing experiments rather than studying research practice."},"grok":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"high","reason":"NMR quantum-computing compiler dissertation; engineering/physics implementation, not metaresearch."}},"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001703028,0.0008063913,0.0006391074,0.0007441055,0.0007054633,0.001010413,0.001518891,0.0007051683,0.004898679],"category_scores_gemma":[0.005255819,0.0006426209,0.0006796042,0.0009877726,0.000610831,0.001423829,0.0009305754,0.001150147,0.002354697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009491619,"about_ca_system_score_gemma":0.002551469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00160876,"about_ca_topic_score_gemma":0.002208713,"domain_scores_codex":[0.9989827,0.0002526063,0.0001633883,0.0001255783,0.0003818955,0.00009374214],"domain_scores_gemma":[0.9977463,0.001073272,0.0001046817,0.0003450105,0.0006708143,0.00005991568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001000451,0.0003446493,0.003043028,0.0006304765,0.00007538497,0.0006618216,0.000685925,0.1153755,0.06485502,0.09656072,0.05384707,0.6629199],"study_design_scores_gemma":[0.0003219506,0.000292327,0.0007857485,0.00008946174,0.00005888137,0.0003371058,0.0001179476,0.809958,0.0749817,0.04532232,0.06765199,0.00008263158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01520399,0.0002409119,0.9403781,0.0002574696,0.0001202032,0.0001939243,0.0004919195,0.03714556,0.00596799],"genre_scores_gemma":[0.08526414,0.000175455,0.9058757,0.000160529,0.00004467079,0.0003109834,0.001090401,0.004640266,0.002437948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004898679,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01312842229553685,"score_gpt":0.2253684328351106,"score_spread":0.2122400105395738,"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."}}