{"id":"W4398231190","doi":"10.1103/prxquantum.5.020341","title":"Quantum-Inspired Classical Algorithm for Graph Problems by Gaussian Boson Sampling","year":2024,"lang":"en","type":"article","venue":"PRX Quantum","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Army Research Office; NTT Research; Natural Sciences and Engineering Research Council of Canada; Multidisciplinary University Research Initiative; U.S. Department of Energy; Air Force Office of Scientific Research; David and Lucile Packard Foundation; Office of Science; National Science Foundation","keywords":"Quantum; Gaussian; Boson; Algorithm; Quantum algorithm; Computer science; Quantum computer; Theoretical computer science; Mathematics; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007265551,0.0004849937,0.0004668527,0.00030373,0.0004764189,0.0009943444,0.00126195,0.0002103161,0.000008214041],"category_scores_gemma":[0.00005973035,0.0004057517,0.0003770482,0.0009605017,0.0001223308,0.0004156569,0.0003199871,0.0005946854,0.00006447959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007052698,"about_ca_system_score_gemma":0.0001671276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004332654,"about_ca_topic_score_gemma":0.000003324477,"domain_scores_codex":[0.9963572,0.0001048756,0.0006061115,0.001320151,0.0005647042,0.00104696],"domain_scores_gemma":[0.9982269,0.0004888181,0.0001242275,0.0007495006,0.00008976636,0.0003207256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009082782,0.0002375411,0.00002287684,0.0003486237,0.0001329117,0.00004833519,0.001238811,0.0009915362,0.00347907,0.1834366,0.01936012,0.7906945],"study_design_scores_gemma":[0.0003265296,0.0003495419,0.00004239522,0.000258711,0.00002042421,0.00005909021,0.00001639185,0.8299032,0.0006288221,0.0697269,0.09816854,0.0004994897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007314559,0.003531811,0.9784189,0.005692189,0.002594292,0.0006519927,0.00008972952,0.001610289,0.0000962313],"genre_scores_gemma":[0.4973133,0.0002006864,0.4981543,0.0009043158,0.001938999,0.0004199655,0.0001617569,0.0002501256,0.0006565794],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8289117,"threshold_uncertainty_score":0.9998394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02207703834229791,"score_gpt":0.2698084289963209,"score_spread":0.247731390654023,"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."}}