{"id":"W2768556639","doi":"","title":"Controlling adaptive quantum-phase estimation with scalable reinforcement learning.","year":2016,"lang":"en","type":"article","venue":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Reinforcement learning; Computer science; Scalability; Workload; Noise (video); Quantum; Construct (python library); Relevance (law); Photon; Phase (matter); Artificial intelligence; Machine learning","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":[0.0009001936,0.0005051132,0.0004624235,0.0002229338,0.0003608003,0.000507782,0.001179086,0.0006080336,0.002032846],"category_scores_gemma":[0.004200897,0.0002522476,0.0002565251,0.0002102071,0.001017776,0.0008492221,0.001048762,0.001068397,0.000369811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008505406,"about_ca_system_score_gemma":0.000980634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002669972,"about_ca_topic_score_gemma":0.002584701,"domain_scores_codex":[0.9996153,0.0001024609,0.00001460608,0.00007613379,0.0001311105,0.00006033429],"domain_scores_gemma":[0.99867,0.0007702592,0.0001567464,0.0001563226,0.0001662498,0.000080438],"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":[0.0001192222,0.0001386411,0.0006282884,0.00004470965,0.00003030549,0.00005811286,0.00003707179,0.9375548,0.008561363,0.01753254,0.001060765,0.03423421],"study_design_scores_gemma":[0.00000902098,0.00001244096,0.0000234278,0.000001069891,0.000001316429,0.000003482936,0.000001108705,0.9968119,0.0007666052,0.002253148,0.0001146469,0.000001790757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03093972,0.0001046808,0.9654173,0.0001921661,0.00006268205,0.00007257611,0.00002383307,0.0006451483,0.002541944],"genre_scores_gemma":[0.8771152,0.00005721775,0.1210368,0.00008852556,0.00002045051,0.000105086,0.00003145948,0.0000574486,0.001487838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002669972,"threshold_uncertainty_score":0.006800532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01622431647145712,"score_gpt":0.2543697345782847,"score_spread":0.2381454181068275,"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."}}