{"id":"W4377238332","doi":"10.1103/physrevlett.130.210601","title":"Quantum Similarity Testing with Convolutional Neural Networks","year":2023,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute","funders":"Ontario Ministry of Research, Innovation and Science; Government of Canada; Croucher Foundation; National Natural Science Foundation of China; Research Grants Council, University Grants Committee; Innovation, Science and Economic Development Canada; John Templeton Foundation","keywords":"Computer science; Quantum state; Convolutional neural network; Algorithm; Gaussian; Quantum; Artificial intelligence; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002510434,0.0001906779,0.0002961341,0.00003863188,0.0001885419,0.00007653607,0.000598651,0.000008433244,0.000001449537],"category_scores_gemma":[0.00008016048,0.0001368088,0.0001071251,0.001248092,0.00008301598,0.0001434673,0.0002534316,0.0003561909,0.0000437245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001757173,"about_ca_system_score_gemma":0.00002321573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009272311,"about_ca_topic_score_gemma":3.607141e-7,"domain_scores_codex":[0.9984782,0.0001110462,0.0001773496,0.000432402,0.0003529746,0.0004480692],"domain_scores_gemma":[0.9988751,0.0004796184,0.00009345324,0.0003867382,0.00005052089,0.0001145889],"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":[0.000006570348,0.0001403534,0.001989654,0.00077342,0.00006913971,0.0002915315,0.0001715312,0.8556249,0.0008555034,0.01292858,0.02124513,0.1059037],"study_design_scores_gemma":[0.00009707324,0.00005433902,0.006288512,0.0004563426,0.00001134491,0.00002567673,4.91147e-7,0.9915102,0.000003169208,0.0004470293,0.0009117813,0.000194115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4422875,0.002317463,0.4898554,0.0629116,0.0005090222,0.0005223299,0.000004797109,0.001507549,0.00008432739],"genre_scores_gemma":[0.9674938,0.0001026063,0.007206528,0.02456138,0.0005767183,0.00002524396,0.00001041152,0.00001978861,0.000003564909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5252063,"threshold_uncertainty_score":0.5578902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02559002461922797,"score_gpt":0.2703011900331146,"score_spread":0.2447111654138867,"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."}}