{"id":"W4280506738","doi":"10.48550/arxiv.2205.07113","title":"Fidelity overhead for non-local measurements in variational quantum algorithms","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Compute Canada","keywords":"Pauli exclusion principle; Qubit; Fidelity; Observable; Algorithm; Quantum; Mathematics; Quantum algorithm; Estimator; Ising model; Unitary state; Computer science; Statistical physics; Quantum mechanics; Physics; Statistics","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":[0.001020678,0.0003888616,0.0004486339,0.0003789619,0.0003249052,0.0001190359,0.002187804,0.0002334326,0.0000373782],"category_scores_gemma":[0.00005893083,0.0004675529,0.0003110023,0.000704598,0.00007405564,0.0002017455,0.002689112,0.0009445456,0.0000100295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005866978,"about_ca_system_score_gemma":0.000564783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004822143,"about_ca_topic_score_gemma":0.00005639503,"domain_scores_codex":[0.997002,0.0002230488,0.0003810163,0.001541174,0.0002950343,0.000557704],"domain_scores_gemma":[0.998078,0.0002504401,0.0003047425,0.001028608,0.000179518,0.000158659],"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":[0.0000360677,0.0002020526,0.001322299,0.00006847799,0.00006665351,0.00006311828,0.0002698687,0.9657121,0.000008740742,0.02872418,0.0002832108,0.003243231],"study_design_scores_gemma":[0.0009237195,0.00009502541,0.006909823,0.00006012051,0.00002089848,0.000003590994,0.00003313357,0.9162633,0.00001289406,0.07466932,0.0005505359,0.0004576662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07960846,0.0000351991,0.9175526,0.0002131805,0.001533335,0.0005738051,0.00007829884,0.0001539363,0.0002511757],"genre_scores_gemma":[0.9731228,0.00001237207,0.02623229,0.0001758832,0.0001503826,0.000009470188,0.00007308545,0.00002483156,0.0001989079],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8935143,"threshold_uncertainty_score":0.9997776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08078720196837035,"score_gpt":0.2237521545732198,"score_spread":0.1429649526048495,"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."}}