{"id":"W7104113099","doi":"","title":"Investigation of Performance and Scalability of a Quantum-Inspired Evolutionary Optimizer (QIEO) on NVIDIA GPU","year":2025,"lang":"","type":"article","venue":"ArXiv.org","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"PQ Corporation (Canada)","funders":"","keywords":"Scalability; Evolutionary algorithm; Kernel (algebra); Probabilistic logic; Knapsack problem; Memory management; Memory footprint; Metaheuristic; Evolutionary computation","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":[0.001748447,0.0003560092,0.0006897299,0.0006188942,0.000280789,0.00005944755,0.0009451392,0.0002383777,0.0001349886],"category_scores_gemma":[0.00138342,0.0003624956,0.0001325657,0.002315752,0.00137807,0.000680336,0.0007455914,0.0004407662,0.0000356093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001643917,"about_ca_system_score_gemma":0.001032101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007823754,"about_ca_topic_score_gemma":0.000001346389,"domain_scores_codex":[0.9955186,0.0006805067,0.001360005,0.0009998474,0.0009457354,0.0004953253],"domain_scores_gemma":[0.9958851,0.0007826339,0.000534865,0.001389484,0.001155463,0.0002524692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002817553,0.0004043888,0.9550858,0.001289597,0.000149533,0.000004973881,0.0009486327,0.01667425,0.001693738,0.009783092,0.0005550293,0.01312922],"study_design_scores_gemma":[0.00069324,0.0002959448,0.447776,0.0003083558,0.00002841558,0.000002018571,0.0000212662,0.544955,0.005356543,0.0003486373,0.00006226688,0.000152346],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8573961,0.0006580795,0.1383514,0.001492566,0.0005351737,0.0007317658,0.00002598622,0.00004324425,0.0007656696],"genre_scores_gemma":[0.9688658,0.0007245743,0.02956042,0.000159999,0.00003847071,0.0000355466,0.00001092894,0.00001667131,0.0005876401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5282807,"threshold_uncertainty_score":0.9998827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04983161367021652,"score_gpt":0.2839702158413225,"score_spread":0.234138602171106,"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."}}