{"id":"W4321484836","doi":"10.48550/arxiv.2202.10601","title":"Quantum Gaussian process model of potential energy surface for a polyatomic molecule","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Statistical physics; Quantum entanglement; Qubit; Quantum; Quantum algorithm; Bayesian optimization; Gaussian; Quantum computer; Quantum process; Ansatz; Computer science; Gaussian process; Quantum state; Algorithm; Mathematics; Quantum mechanics; Physics; Quantum dynamics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009383605,0.0005416521,0.001073848,0.0007593962,0.0007054701,0.001245436,0.002164661,0.002437576,0.004615869],"category_scores_gemma":[0.001609527,0.0003478422,0.0008816599,0.001103516,0.002735519,0.002861382,0.001016875,0.001451929,0.0005844245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001263838,"about_ca_system_score_gemma":0.00107066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004489052,"about_ca_topic_score_gemma":0.00238382,"domain_scores_codex":[0.9995617,0.0001396805,0.00001158123,0.00008988897,0.0001138063,0.00008329886],"domain_scores_gemma":[0.9994004,0.0003023187,0.00005698941,0.00009289529,0.0000836058,0.0000638023],"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":[0.00006789995,0.00005867151,0.0004771664,0.00004906136,0.0000263068,0.0001585859,0.0001098106,0.6297748,0.003328683,0.3623583,0.0007884446,0.002802215],"study_design_scores_gemma":[0.000005318142,0.000007331027,0.00006000206,0.000001738595,0.000002128274,0.00001167264,0.000008328559,0.9714281,0.0001705763,0.02814815,0.0001492844,0.000007372068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1928952,0.0005529977,0.7930626,0.00202876,0.00008818727,0.0000753315,0.0003483357,0.0002454546,0.01070303],"genre_scores_gemma":[0.9481449,0.0004301159,0.03910801,0.0002565064,0.00006386362,0.0001685531,0.0002363758,0.0001076144,0.01148403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004615869,"threshold_uncertainty_score":0.01544166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03069921788636488,"score_gpt":0.1931854304411154,"score_spread":0.1624862125547505,"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."}}