{"id":"W4391097207","doi":"10.1109/access.2024.3357144","title":"Nebula: Network Enhanced Boltzmann Machine With Universal Local Search Architecture","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto","keywords":"Boltzmann machine; Computer science; Ising model; Sampling (signal processing); Mathematical optimization; Quadratic unconstrained binary optimization; Binary number; Simulated annealing; Boltzmann constant; Theoretical computer science; Algorithm; Statistical physics; Mathematics; Artificial neural network; Artificial intelligence; Quantum; Physics; Quantum computer","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005596528,0.0004830827,0.001088888,0.0003486892,0.0003983106,0.0006802353,0.002198423,0.0009995522,0.003271339],"category_scores_gemma":[0.001213697,0.0003487159,0.0004932661,0.0004657979,0.0006419013,0.001131319,0.001161975,0.001078701,0.0009260742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007421174,"about_ca_system_score_gemma":0.00107374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003604305,"about_ca_topic_score_gemma":0.005311223,"domain_scores_codex":[0.9997579,0.00008453189,0.000009220601,0.00004960226,0.00006583922,0.0000329384],"domain_scores_gemma":[0.9997875,0.00008982246,0.00002110985,0.00002911034,0.0000459001,0.00002657115],"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":[0.00008081093,0.00005316805,0.0004508359,0.00008430065,0.00005667725,0.00005393626,0.00003557388,0.9007661,0.002331127,0.02353025,0.002908904,0.06964831],"study_design_scores_gemma":[0.000006726753,0.00001132292,0.00001891102,0.000001824815,0.000002675309,0.000005657767,0.000001272656,0.9962374,0.0002144931,0.003038369,0.0004587542,0.000002580053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01712536,0.0006141085,0.9762895,0.0003528068,0.000109492,0.00006562952,0.00009057859,0.001515591,0.003837022],"genre_scores_gemma":[0.4791456,0.0005107908,0.5094822,0.000711693,0.0001176827,0.0005515104,0.0004048736,0.0003222843,0.008753401],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003604305,"threshold_uncertainty_score":0.01094371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009507163648580442,"score_gpt":0.2557105980384485,"score_spread":0.246203434389868,"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."}}