{"id":"W2981617422","doi":"10.1021/acs.jctc.9b01084","title":"Iterative Qubit Coupled Cluster Approach with Efficient Screening of Generators","year":2020,"lang":"en","type":"preprint","venue":"Journal of Chemical Theory and Computation","topic":"Advanced Thermodynamics and Statistical Mechanics","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto; OTI Lumionics (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Hamiltonian (control theory); Coupled cluster; Ansatz; Ground state; Qubit; Canonical transformation; Quantum; Cluster expansion; Quantum mechanics; Mathematics; Statistical physics; Physics; Applied mathematics; Molecule; Mathematical optimization","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":[],"consensus_categories":[],"category_scores_codex":[0.0002492043,0.0001584972,0.0003967331,0.00003587147,0.00002677359,0.00003193707,0.00009112551,0.00005781434,0.000008019933],"category_scores_gemma":[0.0000165412,0.000117014,0.00008916355,0.00005753385,0.00006164729,0.00002917485,0.00009778779,0.0004371438,1.124854e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001290545,"about_ca_system_score_gemma":0.00004276765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.423731e-7,"about_ca_topic_score_gemma":6.0028e-9,"domain_scores_codex":[0.9990681,0.0001035593,0.0003946454,0.0001577502,0.0001869517,0.00008894334],"domain_scores_gemma":[0.9988102,0.0002342354,0.0006018703,0.00005560892,0.000208144,0.00008999016],"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":[0.001830778,0.0002493343,0.00003926704,0.0001877685,0.00053458,0.000004845067,0.001317804,0.4426074,0.01108894,0.5187227,0.000006101372,0.02341051],"study_design_scores_gemma":[0.0005894104,0.00009371092,0.000007987772,0.0001272462,0.00008854362,0.000003872547,0.000191875,0.6701204,0.003441407,0.3252202,0.000001460887,0.0001138157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.343787,0.0000400198,0.6558981,0.00002119465,0.00003337497,0.00008179839,0.00002222911,0.000002273032,0.0001139803],"genre_scores_gemma":[0.9241058,0.000001169046,0.07565681,0.00002661191,0.0001538451,0.000002691021,0.00003816258,0.00001336947,0.000001562716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5803187,"threshold_uncertainty_score":0.4771694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01159284964020339,"score_gpt":0.2575650795285779,"score_spread":0.2459722298883745,"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."}}