{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003760922,0.0004838122,0.0004492273,0.0002213139,0.0003217672,0.0002754385,0.001958281,0.0002925348,0.000003942625],"category_scores_gemma":[0.00005990057,0.0005477237,0.0003578595,0.0004807371,0.0001018731,0.000167798,0.001609552,0.0007305755,0.0000694917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001700685,"about_ca_system_score_gemma":0.0002870432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006980091,"about_ca_topic_score_gemma":0.000006787551,"domain_scores_codex":[0.9971149,0.000118271,0.0003432768,0.001677471,0.0001691113,0.0005769694],"domain_scores_gemma":[0.9975002,0.0004443241,0.0003981992,0.001273113,0.000179944,0.0002042621],"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":[0.00009964649,0.0000963529,0.00002268987,0.0001919344,0.00009599351,0.0001711597,0.000391548,0.96977,0.00007301805,0.02614415,0.001635049,0.001308426],"study_design_scores_gemma":[0.0005636282,0.0002004077,0.0001135598,0.0002001976,0.00002548235,0.000005057197,0.00001718338,0.9103292,0.0005898711,0.0868023,0.000607728,0.0005453846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3262736,0.000009211019,0.6705198,0.0002426297,0.001647885,0.0004102008,0.00007175283,0.0007223783,0.0001026357],"genre_scores_gemma":[0.9953504,0.00002218372,0.003272809,0.0001330211,0.0003432407,0.000004868158,0.0001066732,0.00004903589,0.0007178159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6690768,"threshold_uncertainty_score":0.9996974,"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."}}