通过机器学习最大化单个和耦合量子晶体记忆器的记忆性

Advanced Quantum Technologies Pub Date : 2024-04-15 DOI:10.1002/qute.202300294

Carlos Hernani‐Morales, Gabriel Alvarado, Francisco Albarrán‐Arriagada, Yolanda Vives‐Gilabert, Enrique Solano, José D. Martín‐Guerrero

{"title":"通过机器学习最大化单个和耦合量子晶体记忆器的记忆性","authors":"Carlos Hernani‐Morales, Gabriel Alvarado, Francisco Albarrán‐Arriagada, Yolanda Vives‐Gilabert, Enrique Solano, José D. Martín‐Guerrero","doi":"10.1002/qute.202300294","DOIUrl":null,"url":null,"abstract":"Machine learning (ML) methods are proposed to characterize the memristive properties of single and coupled quantum memristors. It is shown that maximizing the memristivity leads to large values in the degree of entanglement of two quantum memristors, unveiling the close relationship between quantum correlations and memory. The results strengthen the possibility of using quantum memristors as key components of neuromorphic quantum computing.","PeriodicalId":501028,"journal":{"name":"Advanced Quantum Technologies","volume":"50 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning for Maximizing the Memristivity of Single and Coupled Quantum Memristors\",\"authors\":\"Carlos Hernani‐Morales, Gabriel Alvarado, Francisco Albarrán‐Arriagada, Yolanda Vives‐Gilabert, Enrique Solano, José D. Martín‐Guerrero\",\"doi\":\"10.1002/qute.202300294\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Machine learning (ML) methods are proposed to characterize the memristive properties of single and coupled quantum memristors. It is shown that maximizing the memristivity leads to large values in the degree of entanglement of two quantum memristors, unveiling the close relationship between quantum correlations and memory. The results strengthen the possibility of using quantum memristors as key components of neuromorphic quantum computing.\",\"PeriodicalId\":501028,\"journal\":{\"name\":\"Advanced Quantum Technologies\",\"volume\":\"50 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced Quantum Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/qute.202300294\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Quantum Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/qute.202300294","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

本文提出了机器学习（ML）方法来描述单个和耦合量子忆阻器的忆阻特性。结果表明，忆阻性最大化会导致两个量子忆阻器的纠缠程度达到较大值，从而揭示了量子相关性与记忆之间的密切关系。研究结果加强了将量子忆阻器用作神经形态量子计算关键组件的可能性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Machine Learning for Maximizing the Memristivity of Single and Coupled Quantum Memristors

Machine learning (ML) methods are proposed to characterize the memristive properties of single and coupled quantum memristors. It is shown that maximizing the memristivity leads to large values in the degree of entanglement of two quantum memristors, unveiling the close relationship between quantum correlations and memory. The results strengthen the possibility of using quantum memristors as key components of neuromorphic quantum computing.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Advanced Quantum Technologies

自引率

0.00%

发文量