Quantum Fourier Transformation Using Quantum Reservoir Computing Network

IF 4.4 Q1 OPTICS
Lu-Fan Zhang, Lu Liu, Xing-yu Wu, Chuan Wang
{"title":"Quantum Fourier Transformation Using Quantum Reservoir Computing Network","authors":"Lu-Fan Zhang,&nbsp;Lu Liu,&nbsp;Xing-yu Wu,&nbsp;Chuan Wang","doi":"10.1002/qute.202400396","DOIUrl":null,"url":null,"abstract":"<p>Combining the benefits of quantum computing and artificial neural networks, quantum reservoir computing shows potential for handling complex tasks due to its access to the Hilbert space in exponential dimensions. In this study, the quantum Fourier transform algorithm is implemented utilizing quantum reservoir computing, demonstrating its unique advantages. For the random interactions within the reservoirs, quantum reservoir computing avoids the cost of precise control of the physical system. The proposed model only requires to optimize a linear readout layer, thus significantly reducing the computational cost required for training. The accuracy of the implementation is numerically demonstrated and the model is integrated into quantum circuits to correctly execute the quantum phase estimation algorithm. Additionally, the impacts of different reservoir structures and dissipation intensities within the reservoir, and the results indicate the robustness of the model are discussed.</p>","PeriodicalId":72073,"journal":{"name":"Advanced quantum technologies","volume":"8 3","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced quantum technologies","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/qute.202400396","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0

Abstract

Combining the benefits of quantum computing and artificial neural networks, quantum reservoir computing shows potential for handling complex tasks due to its access to the Hilbert space in exponential dimensions. In this study, the quantum Fourier transform algorithm is implemented utilizing quantum reservoir computing, demonstrating its unique advantages. For the random interactions within the reservoirs, quantum reservoir computing avoids the cost of precise control of the physical system. The proposed model only requires to optimize a linear readout layer, thus significantly reducing the computational cost required for training. The accuracy of the implementation is numerically demonstrated and the model is integrated into quantum circuits to correctly execute the quantum phase estimation algorithm. Additionally, the impacts of different reservoir structures and dissipation intensities within the reservoir, and the results indicate the robustness of the model are discussed.

Abstract Image

基于量子库计算网络的量子傅立叶变换
结合量子计算和人工神经网络的优势,量子储层计算显示出处理复杂任务的潜力,因为它可以在指数维度上访问希尔伯特空间。在本研究中,量子傅里叶变换算法利用量子库计算实现,显示出其独特的优势。对于储层内部的随机相互作用,量子储层计算避免了对物理系统进行精确控制的代价。该模型只需要优化一个线性读出层,从而大大降低了训练所需的计算成本。数值验证了该模型实现的准确性,并将该模型集成到量子电路中以正确执行量子相位估计算法。此外,还讨论了不同储层结构和库内耗散强度对模型的影响,结果表明了模型的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
7.90
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信