{"id":"W3120068356","doi":"10.22331/q-2022-01-18-622","title":"Simulating Effective QED on Quantum Computers","year":2022,"lang":"en","type":"preprint","venue":"Quantum","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"U.S. Department of Energy; Laboratory Directed Research and Development; Pacific Northwest National Laboratory; Basic Energy Sciences; Office of Science; National Science Foundation","keywords":"Physics; Wave function; Scaling; Lattice (music); Momentum (technical analysis); Speedup; Quantum mechanics; Quantum simulator; Electron; Exponential function; Quantum computer; Basis (linear algebra); Quantum; Statistical physics; Mathematical physics; Mathematics; Computer science; Mathematical analysis","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001016152,0.0008908542,0.0009606152,0.0005377379,0.0008144694,0.000597964,0.003583241,0.0003204621,0.00004727157],"category_scores_gemma":[0.0001786854,0.0008595944,0.0005799841,0.0006531068,0.0001023822,0.0001242276,0.006592722,0.003216151,0.00009595438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003102052,"about_ca_system_score_gemma":0.0002389743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001497224,"about_ca_topic_score_gemma":0.00000199603,"domain_scores_codex":[0.9941844,0.0008359124,0.0007329456,0.002122247,0.001137793,0.0009866775],"domain_scores_gemma":[0.9949795,0.001697763,0.0006430691,0.002295497,0.0001134505,0.0002707206],"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":[0.00003742977,0.0002856236,0.0001114378,0.0002164076,0.0001736805,0.0003170001,0.00235991,0.8261449,0.00005611923,0.07226018,0.002450612,0.09558665],"study_design_scores_gemma":[0.0005032303,0.0006590736,0.001341361,0.0003478018,0.0000203332,0.00002329614,0.00003662875,0.9574569,0.00006462705,0.0333803,0.005220759,0.0009456968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3731909,0.0004734402,0.6026188,0.002288093,0.01554997,0.001836245,0.00007130838,0.002288215,0.001683055],"genre_scores_gemma":[0.9726899,0.00001317992,0.02477482,0.00126677,0.0008126225,0.0001559182,0.00007148632,0.0001084244,0.0001068302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.599499,"threshold_uncertainty_score":0.9993855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0145523328122865,"score_gpt":0.2696131950615636,"score_spread":0.255060862249277,"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."}}