{"id":"W4402301242","doi":"10.1109/access.2024.3455997","title":"Inexact Quantum Square Root Circuit for NISQ Devices","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"CMC Microsystems (Canada); Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Square root; Computer science; Root (linguistics); Square (algebra); Quantum computer; Parallel computing; Theoretical computer science; Algorithm; Quantum; Mathematics; Physics","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002830846,0.0002133707,0.000206549,0.0001911319,0.0002125789,0.001651444,0.001904848,0.00007691272,0.000009444323],"category_scores_gemma":[0.00003049252,0.0001735573,0.0001526208,0.0006378755,0.00003064805,0.0007481797,0.0002330015,0.000219395,0.00004732379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002946324,"about_ca_system_score_gemma":0.00011849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005568363,"about_ca_topic_score_gemma":0.00003029683,"domain_scores_codex":[0.998378,0.0000385526,0.0002529718,0.0006342306,0.0002658325,0.0004303564],"domain_scores_gemma":[0.9988437,0.0004082443,0.00005895463,0.0005048245,0.00007614503,0.0001081004],"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":[0.00001660093,0.0001929122,0.001964636,0.001569275,0.0002241414,0.0002994686,0.002611971,0.02541666,0.001101865,0.1661075,0.03699581,0.7634991],"study_design_scores_gemma":[0.0001585469,0.0001006105,0.001622673,0.0001900414,0.00001193172,0.00003940685,0.000006143032,0.9271138,0.001154092,0.03347344,0.03581715,0.0003121679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1109393,0.001115368,0.881566,0.001537585,0.003416542,0.0002662287,0.00001639303,0.0007581816,0.0003844561],"genre_scores_gemma":[0.9959611,0.000004803515,0.002656287,0.0003826417,0.0007850161,0.00003999224,0.000004955521,0.00002764584,0.0001375903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9016972,"threshold_uncertainty_score":0.9993849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03524063424981766,"score_gpt":0.3138244458492912,"score_spread":0.2785838115994735,"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."}}