{"id":"W2949671922","doi":"","title":"Quantum algorithms for the subset-sum problem","year":2013,"lang":"en","type":"preprint","venue":"IACR Cryptology ePrint Archive","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Heuristic; Exponent; Computer science; Algorithm; Subset sum problem; Quantum algorithm; Quantum walk; Discrete mathematics; Quantum; Theoretical computer science; Combinatorics; Mathematics; Artificial intelligence; Quantum mechanics; Physics","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":[0.001020734,0.0004856557,0.0008913471,0.0007165601,0.00106685,0.001831959,0.001435816,0.0008829357,0.008329199],"category_scores_gemma":[0.006002711,0.0002927004,0.0006543613,0.001164019,0.001179846,0.003432568,0.00219046,0.001885385,0.001429516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001132559,"about_ca_system_score_gemma":0.0009716029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005379794,"about_ca_topic_score_gemma":0.0007159379,"domain_scores_codex":[0.9988438,0.0003651183,0.00005464485,0.0001652108,0.0004637315,0.0001074273],"domain_scores_gemma":[0.9974637,0.001243026,0.0001112539,0.0006903067,0.0003525956,0.0001390708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000230135,0.0001089866,0.0003356067,0.0001352446,0.00004871253,0.00004315065,0.0001329444,0.0488679,0.003253536,0.8337378,0.008980089,0.1041258],"study_design_scores_gemma":[0.00005785423,0.00004211583,0.0001048861,0.0000167768,0.00001427445,0.00004478305,0.00003200577,0.2970843,0.002621708,0.6959061,0.004061445,0.00001365962],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07876325,0.0007290985,0.8906873,0.001992725,0.0003273608,0.0001545765,0.0002570965,0.00097031,0.02611827],"genre_scores_gemma":[0.6394871,0.0006704691,0.3439924,0.0005401966,0.0003595837,0.0002365467,0.0005026052,0.0003617596,0.01384921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008329199,"threshold_uncertainty_score":0.02786398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02324731095667313,"score_gpt":0.2686494261659161,"score_spread":0.245402115209243,"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."}}