{"id":"W4391013485","doi":"10.48550/arxiv.2401.09253","title":"The generative quantum eigensolver (GQE) and its application for ground state search","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Advanced Research Projects Agency; Japan Society for the Promotion of Science; Office of Science; Defense Advanced Research Projects Agency; Council for Science, Technology and Innovation; National Energy Research Scientific Computing Center; Canadian Institute for Advanced Research; U.S. Department of Energy","keywords":"Generative grammar; Quantum; Transformer; Hamiltonian (control theory); Computer science; Quantum computer; Ground state; Generative model; Artificial intelligence; Quantum mechanics; Mathematics; Physics; Mathematical optimization","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004497443,0.0002722467,0.000208507,0.0001258757,0.0005786158,0.0003715754,0.001120402,0.0001314275,7.183433e-7],"category_scores_gemma":[0.00001477308,0.0002231715,0.000135953,0.0003569588,0.0001148914,0.00009101805,0.002456876,0.0006314294,0.00002496267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009144492,"about_ca_system_score_gemma":0.0001965691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006453625,"about_ca_topic_score_gemma":0.00003081825,"domain_scores_codex":[0.9981197,0.0001185095,0.0001653759,0.001105032,0.0001035346,0.0003878384],"domain_scores_gemma":[0.9986027,0.0003072732,0.0001088125,0.0006689384,0.0001905692,0.0001217332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002676718,0.00002982006,0.00002527212,0.0001699204,0.0001463556,0.00004695894,0.001226565,0.4028107,0.000190636,0.5787942,0.0002067463,0.01632603],"study_design_scores_gemma":[0.0001294696,0.00004190396,0.000110786,0.00003332407,0.00002225986,0.000004063324,0.0000302838,0.7900191,0.0001849478,0.2080989,0.001107767,0.0002171932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3958292,0.0006438518,0.6016892,0.0005665147,0.0004375128,0.0005903977,0.0000305339,0.0001493657,0.00006346844],"genre_scores_gemma":[0.9966353,0.0003304295,0.001507516,0.00007077053,0.0001671251,0.000006616718,0.00001012422,0.00002285009,0.001249291],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6008061,"threshold_uncertainty_score":0.910067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04961022222098412,"score_gpt":0.213401072730324,"score_spread":0.1637908505093399,"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."}}