{"id":"W2725047442","doi":"10.1103/physreva.97.022315","title":"Continuous-variable quantum Gaussian process regression and quantum singular value decomposition of nonsparse low-rank matrices","year":2018,"lang":"en","type":"article","venue":"Physical review. A/Physical review, A","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Xanadu Quantum Technologies (Canada)","funders":"Office of Naval Research; Louisiana State University","keywords":"Subroutine; Quantum computer; Quantum algorithm; Gaussian process; Computer science; Singular value decomposition; Gaussian; Algorithm; Kriging; Quantum process; Mathematics; Applied mathematics; Mathematical optimization; Quantum; Machine learning; Quantum mechanics; Physics; Quantum dynamics","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":[0.0008687584,0.0005699837,0.0004407859,0.000585988,0.000423541,0.0007450745,0.0008586117,0.0007214865,0.005659179],"category_scores_gemma":[0.003681362,0.0002588205,0.0005614686,0.0007046292,0.0009979251,0.001285746,0.0007261646,0.0014259,0.001719382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005368001,"about_ca_system_score_gemma":0.0008134941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001562098,"about_ca_topic_score_gemma":0.001966824,"domain_scores_codex":[0.9995603,0.0001545056,0.00001693892,0.00007273626,0.0001555506,0.00004008077],"domain_scores_gemma":[0.9991829,0.000440574,0.00007892299,0.0001240022,0.0001374301,0.00003606308],"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":[0.00008270777,0.00009300646,0.0006520806,0.0001818226,0.00003782509,0.0001608144,0.0001606559,0.2079154,0.01014015,0.6425675,0.006187677,0.1318205],"study_design_scores_gemma":[0.00000901066,0.00001504772,0.00009633743,0.00001004265,0.000002747808,0.00003771718,0.000009423075,0.9213253,0.003278745,0.0725265,0.002678029,0.0000110355],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004040815,0.00009601795,0.9929831,0.0001378836,0.00002822901,0.00001900836,0.00003202975,0.0003616995,0.002301241],"genre_scores_gemma":[0.133954,0.0003082793,0.8593393,0.0001404453,0.00007447699,0.0001029474,0.0001364752,0.0003170822,0.005626962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005659179,"threshold_uncertainty_score":0.01893187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008607886062495018,"score_gpt":0.336435898160848,"score_spread":0.3278280120983529,"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."}}