{"id":"W3048801116","doi":"10.1103/physreva.103.042405","title":"Improving Hamiltonian encodings with the Gray code","year":2021,"lang":"en","type":"article","venue":"Physical review. A/Physical review, A","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; TRIUMF","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Gray (unit); Gray code; Computer science; Programming language; Mathematics; Algorithm; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006059689,0.0005698622,0.001344793,0.00002775026,0.0003948215,0.0002312694,0.001813421,0.00003021358,0.000009446117],"category_scores_gemma":[0.0004928081,0.0003117331,0.0007077809,0.001654853,0.0002048048,0.0003022759,0.0008235466,0.0009201241,0.0002114106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005376501,"about_ca_system_score_gemma":0.0002503146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001904663,"about_ca_topic_score_gemma":0.000009362653,"domain_scores_codex":[0.9960835,0.000500629,0.0004754674,0.001168209,0.0009904512,0.0007817401],"domain_scores_gemma":[0.9964925,0.0006917219,0.0003806923,0.001796917,0.0003097796,0.0003284071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000857549,0.001181187,0.00007266301,0.01101425,0.0001910118,0.0002807262,0.0007379823,0.0001246472,0.02823999,0.09969791,0.01147307,0.846978],"study_design_scores_gemma":[0.001002554,0.0009679314,0.001168497,0.04354466,0.001001126,0.0004384622,0.00002256549,0.4066026,0.02360553,0.03922418,0.479555,0.002866929],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1679223,0.2997561,0.4240671,0.09891661,0.0006157616,0.003368213,0.00004012218,0.001089647,0.004224065],"genre_scores_gemma":[0.8624911,0.07260915,0.01900587,0.04297647,0.002050613,0.0003983187,0.00002779911,0.0001432874,0.0002973433],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8441111,"threshold_uncertainty_score":0.9999335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00956348489008665,"score_gpt":0.3003099071900789,"score_spread":0.2907464222999923,"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."}}