{"title":"Optimizing Metrological Devices with Memory-Efficient Automatic Differentiation","authors":"M. Goerz, Sebastián C Carrasco, V. Malinovsky","doi":"10.1364/quantum.2022.qw2a.12","DOIUrl":null,"url":null,"abstract":"We present a computational framework for the direct optimization of measures of metrological gain without analytic gradients, such as the quantum Fisher information. The method is enabled by a new memory-efficient formulation of automatic differentiation.","PeriodicalId":369002,"journal":{"name":"Quantum 2.0 Conference and Exhibition","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quantum 2.0 Conference and Exhibition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/quantum.2022.qw2a.12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
We present a computational framework for the direct optimization of measures of metrological gain without analytic gradients, such as the quantum Fisher information. The method is enabled by a new memory-efficient formulation of automatic differentiation.
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