{"title":"量子态学习复杂性调查","authors":"Anurag Anshu, Srinivasan Arunachalam","doi":"10.1038/s42254-023-00662-4","DOIUrl":null,"url":null,"abstract":"Quantum learning theory is a new and very active area of research at the intersection of quantum computing and machine learning. Important breakthroughs in the past two years have rapidly solidified its foundations and led to a need for an encompassing survey that can be read by seasoned and early-career researchers in quantum computing. In this Perspective, we survey various results that rigorously study the complexity of learning quantum states. These include progress on quantum tomography, learning physical quantum states, alternative learning models to tomography, and learning classical functions encoded as quantum states. We highlight how these results are leading towards a successful theory with a range of exciting open questions, some of which we list throughout the text. Quantum learning theory is a new and very active area of research at the intersection of quantum computing and machine learning. This Perspective surveys the progress in this field, highlighting a number of exciting open questions.","PeriodicalId":19024,"journal":{"name":"Nature Reviews Physics","volume":null,"pages":null},"PeriodicalIF":44.8000,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A survey on the complexity of learning quantum states\",\"authors\":\"Anurag Anshu, Srinivasan Arunachalam\",\"doi\":\"10.1038/s42254-023-00662-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Quantum learning theory is a new and very active area of research at the intersection of quantum computing and machine learning. Important breakthroughs in the past two years have rapidly solidified its foundations and led to a need for an encompassing survey that can be read by seasoned and early-career researchers in quantum computing. In this Perspective, we survey various results that rigorously study the complexity of learning quantum states. These include progress on quantum tomography, learning physical quantum states, alternative learning models to tomography, and learning classical functions encoded as quantum states. We highlight how these results are leading towards a successful theory with a range of exciting open questions, some of which we list throughout the text. Quantum learning theory is a new and very active area of research at the intersection of quantum computing and machine learning. This Perspective surveys the progress in this field, highlighting a number of exciting open questions.\",\"PeriodicalId\":19024,\"journal\":{\"name\":\"Nature Reviews Physics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":44.8000,\"publicationDate\":\"2023-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Reviews Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.nature.com/articles/s42254-023-00662-4\",\"RegionNum\":1,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHYSICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Reviews Physics","FirstCategoryId":"101","ListUrlMain":"https://www.nature.com/articles/s42254-023-00662-4","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSICS, APPLIED","Score":null,"Total":0}
A survey on the complexity of learning quantum states
Quantum learning theory is a new and very active area of research at the intersection of quantum computing and machine learning. Important breakthroughs in the past two years have rapidly solidified its foundations and led to a need for an encompassing survey that can be read by seasoned and early-career researchers in quantum computing. In this Perspective, we survey various results that rigorously study the complexity of learning quantum states. These include progress on quantum tomography, learning physical quantum states, alternative learning models to tomography, and learning classical functions encoded as quantum states. We highlight how these results are leading towards a successful theory with a range of exciting open questions, some of which we list throughout the text. Quantum learning theory is a new and very active area of research at the intersection of quantum computing and machine learning. This Perspective surveys the progress in this field, highlighting a number of exciting open questions.
期刊介绍:
Nature Reviews Physics is an online-only reviews journal, part of the Nature Reviews portfolio of journals. It publishes high-quality technical reference, review, and commentary articles in all areas of fundamental and applied physics. The journal offers a range of content types, including Reviews, Perspectives, Roadmaps, Technical Reviews, Expert Recommendations, Comments, Editorials, Research Highlights, Features, and News & Views, which cover significant advances in the field and topical issues. Nature Reviews Physics is published monthly from January 2019 and does not have external, academic editors. Instead, all editorial decisions are made by a dedicated team of full-time professional editors.