{"id":"W3126478155","doi":"10.1109/tqe.2021.3057799","title":"Quantum Engineering With Hybrid Magnonic Systems and Materials <i>(Invited Paper)</i>","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Quantum Engineering","topic":"Ferroelectric and Piezoelectric Materials","field":"Materials Science","cited_by":145,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Basic Energy Sciences; Division of Electrical, Communications and Cyber Systems; Air Force Office of Scientific Research; National Science Foundation of Sri Lanka; Engineering and Physical Sciences Research Council; U.S. Department of Energy; Division of Materials Research; Office of Science; Division of Materials Sciences and Engineering; University of Chicago; National Science Foundation","keywords":"Magnon; Quantum; Quantum technology; Magnonics; Quantum sensor; Quantum network; Quantum optics; Quantum computer; Quantum information; Physics; Open quantum system; Quantum mechanics; Electron","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003828606,0.0004670778,0.0006085299,0.0002308636,0.0001776964,0.0003475848,0.0001904303,0.000128887,0.0003024502],"category_scores_gemma":[0.00002719374,0.0004323986,0.00006535898,0.0004726765,0.00003320236,0.0003611245,0.000004271274,0.0002495616,0.0000688518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001086468,"about_ca_system_score_gemma":0.00008271351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001217274,"about_ca_topic_score_gemma":0.000004547618,"domain_scores_codex":[0.9976209,0.00008592146,0.0005496148,0.000628966,0.0003787343,0.0007358258],"domain_scores_gemma":[0.9988697,0.0002474463,0.0001004466,0.0004638991,0.0001028359,0.0002156988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007802423,0.00006485175,6.987141e-7,0.0003107836,0.00005029909,0.0001535571,0.00005817655,0.07138949,0.9267285,0.000898846,0.00008485492,0.000181852],"study_design_scores_gemma":[0.0006796406,0.0002616229,0.00002705566,0.0002612865,0.00009223987,0.0008048437,0.00003086176,0.09005421,0.9055941,0.00001413665,0.001577863,0.0006021861],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6264234,0.0005958595,0.3704423,0.00009357833,0.0016676,0.0002502363,0.0001025324,0.0004106697,0.0000138172],"genre_scores_gemma":[0.9980717,0.000324156,0.0009777331,0.0001040433,0.0001483085,0.0001500479,0.00001240998,0.000107836,0.0001037845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3716482,"threshold_uncertainty_score":0.9998128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007752542621868329,"score_gpt":0.186207892753973,"score_spread":0.1784553501321047,"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."}}