{"id":"W3049070297","doi":"10.1103/physreva.103.012405","title":"Estimating the gradient and higher-order derivatives on quantum hardware","year":2021,"lang":"en","type":"article","venue":"Physical review. A/Physical review, A","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":154,"is_retracted":false,"has_abstract":true,"ca_institutions":"Xanadu Quantum Technologies (Canada)","funders":"Defense Advanced Research Projects Agency","keywords":"Hessian matrix; Estimator; Metric (unit); Applied mathematics; Quantum circuit; Computer science; Quantum; Hyperparameter; Simple (philosophy); Tensor (intrinsic definition); Divergence (linguistics); Quantum algorithm; Algorithm; Quantum phase estimation algorithm; Quantum computer; Mathematical optimization; Mathematics; Quantum error correction; Pure mathematics; Quantum mechanics; Statistics; Physics","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":[0.0009213164,0.0009182628,0.0007572587,0.000723196,0.0004300686,0.001472337,0.0011578,0.001156134,0.002398216],"category_scores_gemma":[0.007092982,0.0005223281,0.0004342377,0.0006833714,0.00130304,0.003105793,0.000890704,0.001467693,0.0005128571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009450566,"about_ca_system_score_gemma":0.0006634052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002343535,"about_ca_topic_score_gemma":0.002198403,"domain_scores_codex":[0.9994419,0.0001523993,0.00002475019,0.000112271,0.0002162326,0.00005240884],"domain_scores_gemma":[0.99803,0.001192898,0.0001526328,0.0003454835,0.0002240903,0.00005478795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002404162,0.0001214791,0.00280325,0.0002267942,0.0001019116,0.0002020706,0.0002016275,0.4538764,0.03779313,0.3808126,0.002983487,0.1206369],"study_design_scores_gemma":[0.000008754718,0.00004928492,0.0007757623,0.00001400909,0.00001189435,0.00005785392,0.00001750195,0.8989232,0.009543063,0.08916858,0.001401184,0.00002896527],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07995658,0.0008877123,0.9122447,0.0007099377,0.0001734279,0.00003090287,0.00009369214,0.0006536628,0.005249352],"genre_scores_gemma":[0.6808307,0.0009690614,0.3102298,0.0001616779,0.0001029878,0.0000362249,0.0001472789,0.0002507748,0.007271511],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002398216,"threshold_uncertainty_score":0.008022845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01875917131793193,"score_gpt":0.3334229514083197,"score_spread":0.3146637800903878,"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."}}