{"id":"W4388092466","doi":"10.48550/arxiv.2310.18410","title":"Initial state preparation for quantum chemistry on quantum computers","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Alliance de recherche numérique du Canada; Mitacs; Ministry of Education, Science and Technology; Stewart Blusson Quantum Matter Institute, University of British Columbia; National Research Foundation of Korea; Agencia Estatal de Investigación; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Ground state; Computer science; State (computer science); Statistical physics; Metric (unit); Scaling; Ansatz; Quality (philosophy); Quantum; Mathematical optimization; Energy (signal processing); Work (physics); Algorithm; Mathematics; Physics; Quantum mechanics","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":[0.002389751,0.0006906994,0.0007417625,0.0007751837,0.001119851,0.00210342,0.001473585,0.001080029,0.006976513],"category_scores_gemma":[0.009657037,0.0005948824,0.0005297566,0.001156725,0.001791266,0.003935164,0.002281117,0.003008323,0.002092249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001568712,"about_ca_system_score_gemma":0.001851278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001084502,"about_ca_topic_score_gemma":0.001314986,"domain_scores_codex":[0.9985439,0.0006680824,0.00007679251,0.0001511424,0.0004544526,0.0001057155],"domain_scores_gemma":[0.997377,0.001198595,0.0001151063,0.0009066513,0.0003259263,0.00007679588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002488771,0.0001165001,0.0006517059,0.0002085244,0.00003256344,0.00006923837,0.0001930466,0.1558335,0.009380973,0.6378441,0.005789177,0.1896318],"study_design_scores_gemma":[0.0000280688,0.00005168934,0.0001094602,0.00003949972,0.000008696155,0.00003346867,0.00003331101,0.6039981,0.01038496,0.3769221,0.00837086,0.00001982246],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006508341,0.0002648884,0.9880002,0.0003437935,0.00005425133,0.0000622093,0.00003793686,0.001084044,0.003644361],"genre_scores_gemma":[0.211008,0.0004539513,0.7840334,0.0001783792,0.00006306064,0.0002402354,0.0002255622,0.0005543074,0.003243038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006976513,"threshold_uncertainty_score":0.02333879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06747862802982367,"score_gpt":0.229424960423263,"score_spread":0.1619463323934393,"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."}}