{"id":"W4402915697","doi":"10.1109/tcomm.2024.3469555","title":"Quantum Property Learning for NISQ Networks: Universal Quantum Witness Machines","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Queen's University; Kyung Hee University; Queen's University Belfast","keywords":"Computer science; Property (philosophy); Quantum; Quantum computer; Theoretical computer science; Quantum network; Physics; Quantum mechanics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003287891,0.000238966,0.0002047246,0.0002553339,0.001348166,0.0003734767,0.001857405,0.00009846535,0.000009229315],"category_scores_gemma":[0.00001193276,0.0001821335,0.0001955396,0.0009932311,0.0001466056,0.0003385031,0.00002916956,0.0008545629,0.00003540964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006786729,"about_ca_system_score_gemma":0.0001561835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001336094,"about_ca_topic_score_gemma":0.00008991359,"domain_scores_codex":[0.9985001,0.0002201865,0.0002914112,0.0004547543,0.0001825586,0.0003509943],"domain_scores_gemma":[0.9972842,0.0009677398,0.00005950799,0.001444367,0.0001364031,0.0001078345],"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":[0.0000158383,0.0002282777,0.000002270759,0.00005237152,0.0001059814,0.000004327459,0.001235933,0.691659,0.0002029565,0.05725866,0.0004750359,0.2487593],"study_design_scores_gemma":[0.0002045175,0.0001591221,0.00001218892,0.0001537343,0.00003274696,0.00003143029,0.00005437729,0.9708807,0.000102391,0.003399076,0.0247088,0.0002608948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007032484,0.0006145456,0.985176,0.0105739,0.001181041,0.0003766208,0.00001540204,0.001126382,0.0002328475],"genre_scores_gemma":[0.9682091,0.0002497674,0.03016233,0.0001771293,0.00008835524,0.0001228915,0.0000139682,0.00004258534,0.0009338436],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9675059,"threshold_uncertainty_score":0.999952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02382551532390113,"score_gpt":0.2705438237494689,"score_spread":0.2467183084255678,"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."}}