{"id":"W4385236030","doi":"10.1021/acs.jctc.3c00335","title":"Partitioning Quantum Chemistry Simulations with Clifford Circuits","year":2023,"lang":"en","type":"article","venue":"Journal of Chemical Theory and Computation","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Vector Institute; University of Toronto","funders":"Laboratory Directed Research and Development; Natural Sciences and Engineering Research Council of Canada; Los Alamos National Laboratory; National Nuclear Security Administration; Office of Science; Vector Institute; Canada Research Chairs; Google; Compute Canada; Canadian Institute for Advanced Research; Government of Canada; Department of Energy and Climate Change; U.S. Department of Energy","keywords":"Quantum chemistry; Computer science; Quantum; Chemistry; Electronic circuit; Quantum chemical; Nanotechnology; Computational chemistry; Chemical physics; Physics; Molecule; Materials science; Quantum mechanics; Organic chemistry","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":[0.0004671217,0.00008674544,0.0001401325,0.00006208658,0.0001012839,0.00009842381,0.0001729223,0.00004056175,0.00000317747],"category_scores_gemma":[0.00007966234,0.0000664137,0.00004420973,0.0003710923,0.00005201474,0.000192719,0.00005493807,0.0001935849,0.000002066934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001152366,"about_ca_system_score_gemma":0.00003908803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.746413e-7,"about_ca_topic_score_gemma":1.325561e-8,"domain_scores_codex":[0.9992118,0.0000644648,0.0002486683,0.0001307542,0.000204949,0.0001393323],"domain_scores_gemma":[0.9990128,0.0005009126,0.0002011629,0.00007926062,0.000120689,0.00008521496],"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":[0.0001453098,0.0001731604,0.000533196,0.0001454352,0.0001670135,0.0001563316,0.003180715,0.5316547,0.1106241,0.07987545,0.0003292947,0.2730154],"study_design_scores_gemma":[0.0004451294,0.0000836671,0.0004452,0.0001055696,0.00001264739,0.0002540454,0.00004552738,0.7750388,0.01188213,0.2114756,0.00009233018,0.0001193405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5825269,0.0000299246,0.4170471,0.0002365842,0.00005091444,0.00001906648,6.121107e-7,0.00004423873,0.00004466979],"genre_scores_gemma":[0.995891,0.000003087023,0.003870243,0.00006973515,0.0001462459,5.330294e-7,0.000003497804,0.000005974302,0.000009717621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4133641,"threshold_uncertainty_score":0.2708272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.012526565071857,"score_gpt":0.2592058005996753,"score_spread":0.2466792355278183,"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."}}