{"id":"W4390092954","doi":"10.48550/arxiv.2312.13282","title":"Estimating Trotter Approximation Errors to Optimize Hamiltonian Partitioning for Lower Eigenvalue Errors","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs; University of Toronto","keywords":"Eigenvalues and eigenvectors; Hamiltonian (control theory); Mathematics; Applied mathematics; Approximation error; Perturbation theory (quantum mechanics); Quantum; Perturbation (astronomy); Mathematical optimization; Quantum mechanics; Physics","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":[0.002172758,0.0006627407,0.0006243697,0.0007302184,0.0004129429,0.0007947134,0.0008549645,0.0007174306,0.001147827],"category_scores_gemma":[0.01528247,0.0003359227,0.000336572,0.0005132657,0.0007356773,0.001447129,0.0009749393,0.00115156,0.0003844815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005684347,"about_ca_system_score_gemma":0.001105747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001683583,"about_ca_topic_score_gemma":0.002497465,"domain_scores_codex":[0.9991508,0.0003959907,0.00004078486,0.00009769999,0.0002389067,0.00007584077],"domain_scores_gemma":[0.9941518,0.00406793,0.0004025838,0.0008039916,0.000450276,0.0001235273],"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":[0.0002245036,0.0001365303,0.002346952,0.0001609394,0.00007282374,0.0000551075,0.000197109,0.8915263,0.02473579,0.02319986,0.0006934904,0.05665057],"study_design_scores_gemma":[0.000005146968,0.00003719825,0.0002798214,0.000009447142,0.000003892735,0.0000102772,0.00001644876,0.9884716,0.00564481,0.005348707,0.0001646624,0.000007940776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1017666,0.0002068755,0.8960047,0.0001522685,0.00003545286,0.00005350583,0.00005712061,0.0004749444,0.001248513],"genre_scores_gemma":[0.5721551,0.0001268058,0.4260293,0.00006052274,0.00001751697,0.0001309061,0.0001834556,0.0003590465,0.0009373177],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002172758,"threshold_uncertainty_score":0.01149076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07155294869942376,"score_gpt":0.2205894400829907,"score_spread":0.1490364913835669,"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."}}