Xuchuang Wang, Yu-Zhen Janice Chen, Matheus Guedes de Andrade, Mohammad Hajiesmaili, John C.S. Lui, Don Towsley
{"title":"量子最佳臂辨识","authors":"Xuchuang Wang, Yu-Zhen Janice Chen, Matheus Guedes de Andrade, Mohammad Hajiesmaili, John C.S. Lui, Don Towsley","doi":"10.1145/3626570.3626596","DOIUrl":null,"url":null,"abstract":"Recent progress on building quantum computers [1] envisages wide applications of quantum algorithms in the near future. With the advantage of quantum computer, one can speed up not only fundamental algorithms, e.g., unstructured search [6] and factoring [11], but recent machine learning algorithms [3] as well. In this paper, we study the quantum speedup on a canonical task of reinforcement learning-best arm identification in multi-armed bandits.","PeriodicalId":35745,"journal":{"name":"Performance Evaluation Review","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quantum Best Arm Identification\",\"authors\":\"Xuchuang Wang, Yu-Zhen Janice Chen, Matheus Guedes de Andrade, Mohammad Hajiesmaili, John C.S. Lui, Don Towsley\",\"doi\":\"10.1145/3626570.3626596\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent progress on building quantum computers [1] envisages wide applications of quantum algorithms in the near future. With the advantage of quantum computer, one can speed up not only fundamental algorithms, e.g., unstructured search [6] and factoring [11], but recent machine learning algorithms [3] as well. In this paper, we study the quantum speedup on a canonical task of reinforcement learning-best arm identification in multi-armed bandits.\",\"PeriodicalId\":35745,\"journal\":{\"name\":\"Performance Evaluation Review\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Performance Evaluation Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3626570.3626596\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Performance Evaluation Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3626570.3626596","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
Recent progress on building quantum computers [1] envisages wide applications of quantum algorithms in the near future. With the advantage of quantum computer, one can speed up not only fundamental algorithms, e.g., unstructured search [6] and factoring [11], but recent machine learning algorithms [3] as well. In this paper, we study the quantum speedup on a canonical task of reinforcement learning-best arm identification in multi-armed bandits.
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