{"title":"假设一个量子数据集","authors":"M. Kieferová, Y. Sanders","doi":"10.1162/99608f92.69c5328d","DOIUrl":null,"url":null,"abstract":". Data-processing algorithms often require that the data is prepared in appropriate structures that are readily accessible or can be prepared on demand. Quantum computers derive their power from storing and manipulating quantum superpositions and could potentially speed up data science tasks. However, they often require input in the form of a quantum state that encodes a nonquantum data set. Here we describe some of the challenges of encoding nonquantum data for use by quantum computers","PeriodicalId":73195,"journal":{"name":"Harvard data science review","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Assume a Quantum Dataset\",\"authors\":\"M. Kieferová, Y. Sanders\",\"doi\":\"10.1162/99608f92.69c5328d\",\"DOIUrl\":null,\"url\":null,\"abstract\":\". Data-processing algorithms often require that the data is prepared in appropriate structures that are readily accessible or can be prepared on demand. Quantum computers derive their power from storing and manipulating quantum superpositions and could potentially speed up data science tasks. However, they often require input in the form of a quantum state that encodes a nonquantum data set. Here we describe some of the challenges of encoding nonquantum data for use by quantum computers\",\"PeriodicalId\":73195,\"journal\":{\"name\":\"Harvard data science review\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Harvard data science review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1162/99608f92.69c5328d\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Harvard data science review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1162/99608f92.69c5328d","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
. Data-processing algorithms often require that the data is prepared in appropriate structures that are readily accessible or can be prepared on demand. Quantum computers derive their power from storing and manipulating quantum superpositions and could potentially speed up data science tasks. However, they often require input in the form of a quantum state that encodes a nonquantum data set. Here we describe some of the challenges of encoding nonquantum data for use by quantum computers