{"title":"优化计量设备与内存高效自动区分","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":"{\"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}","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}
Optimizing Metrological Devices with Memory-Efficient Automatic Differentiation
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
