{"id":"W2595997627","doi":"10.48550/arxiv.1010.0009","title":"Unstructured Randomness, Small Gaps and Localization","year":2010,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Laboratory; Natural Sciences and Engineering Research Council of Canada; Army Research Office; W. M. Keck Foundation; National Science Foundation","keywords":"Randomness; Adiabatic quantum computation; Ground state; Hamiltonian (control theory); Adiabatic process; Mathematics; Quantum annealing; Quantum; Function (biology); Random function; Satisfiability; Statistical physics; Algorithm; Discrete mathematics; Quantum computer; Quantum mechanics; Physics; Random variable; Mathematical optimization; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008122826,0.000275688,0.0004358948,0.0004558978,0.0007178847,0.0009167739,0.0008447816,0.0009397196,0.002068489],"category_scores_gemma":[0.005897378,0.0002282388,0.0003419458,0.0003052206,0.002653268,0.002018739,0.001058074,0.001100361,0.0001473906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008671874,"about_ca_system_score_gemma":0.0005098532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001034987,"about_ca_topic_score_gemma":0.000750057,"domain_scores_codex":[0.9996264,0.0001445141,0.00001124604,0.00004944329,0.00007196248,0.00009645379],"domain_scores_gemma":[0.9962428,0.002788996,0.0003919113,0.0002579359,0.0001278345,0.0001905854],"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":[0.0001220382,0.00006162011,0.0008652591,0.00006638609,0.00003301633,0.0002233271,0.0001755665,0.2345408,0.004470166,0.7550374,0.0006242191,0.003780131],"study_design_scores_gemma":[0.00003339194,0.00005944611,0.0003177224,0.00001016338,0.000009057082,0.00006120984,0.00006658842,0.6412046,0.001312951,0.3565108,0.0003985715,0.0000155157],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8136213,0.0004633551,0.1709551,0.001535146,0.00005464655,0.00002927439,0.00006320036,0.0001633648,0.01311467],"genre_scores_gemma":[0.9928107,0.00007393542,0.005659735,0.00006314023,0.00001754063,0.00002176668,0.00002050058,0.00002865092,0.001304092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002068489,"threshold_uncertainty_score":0.006919801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01623024332323293,"score_gpt":0.151859783860869,"score_spread":0.1356295405376361,"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."}}