Integrated Circuits for Quantum Machine Learning Based on Superconducting Artificial Atoms and Methods of Their Control

IF 0.8 4区 地球科学 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Radiophysics and Quantum Electronics Pub Date : 2024-10-18 DOI:10.1007/s11141-024-10342-7
A. E. Tolstobrov, Sh. V. Kadyrmetov, G. P. Fedorov, S. V. Sanduleanu, V. B. Lubsanov, D. A. Kalacheva, A. N. Bolgar, A. Yu. Dmitriev, E. V. Korostylev, K. S. Tikhonov, O. V. Astafiev
{"title":"Integrated Circuits for Quantum Machine Learning Based on Superconducting Artificial Atoms and Methods of Their Control","authors":"A. E. Tolstobrov,&nbsp;Sh. V. Kadyrmetov,&nbsp;G. P. Fedorov,&nbsp;S. V. Sanduleanu,&nbsp;V. B. Lubsanov,&nbsp;D. A. Kalacheva,&nbsp;A. N. Bolgar,&nbsp;A. Yu. Dmitriev,&nbsp;E. V. Korostylev,&nbsp;K. S. Tikhonov,&nbsp;O. V. Astafiev","doi":"10.1007/s11141-024-10342-7","DOIUrl":null,"url":null,"abstract":"<p>This paper is devoted to the use of quantum integrated circuits based on superconducting artificial atoms to solve quantum machine learning problems. The process of designing such chips is de- scribed in detail, including the selection of the most important geometric parameters of the device, as well as numerical calculations of electromagnetic characteristics. The process of controlling a quantum integrated circuit is described. Much attention is paid to the implementation of single- and two-qubit operations. The qubit state readout procedure is also described. A brief introduction into the field of quantum machine learning is given. An algorithm that makes it possible to solve multilabel classification problems using quantum integrated circuits is described. The selection of optimal quantum circuits for the implementation of this algorithm is made using numerical simulations. The operation of the algorithm is demonstrated by the example of standard datasets. Obtained experimental results are compared with the results of theoretical calculations.</p>","PeriodicalId":748,"journal":{"name":"Radiophysics and Quantum Electronics","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiophysics and Quantum Electronics","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s11141-024-10342-7","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

This paper is devoted to the use of quantum integrated circuits based on superconducting artificial atoms to solve quantum machine learning problems. The process of designing such chips is de- scribed in detail, including the selection of the most important geometric parameters of the device, as well as numerical calculations of electromagnetic characteristics. The process of controlling a quantum integrated circuit is described. Much attention is paid to the implementation of single- and two-qubit operations. The qubit state readout procedure is also described. A brief introduction into the field of quantum machine learning is given. An algorithm that makes it possible to solve multilabel classification problems using quantum integrated circuits is described. The selection of optimal quantum circuits for the implementation of this algorithm is made using numerical simulations. The operation of the algorithm is demonstrated by the example of standard datasets. Obtained experimental results are compared with the results of theoretical calculations.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于超导人工原子的量子机器学习集成电路及其控制方法
本文主要介绍利用基于超导人工原子的量子集成电路来解决量子机器学习问题。本文详细描述了此类芯片的设计过程,包括器件最重要几何参数的选择以及电磁特性的数值计算。还介绍了量子集成电路的控制过程。其中非常关注单量子比特和双量子比特操作的实现。此外,还介绍了量子比特状态读出程序。简要介绍了量子机器学习领域。介绍了一种利用量子集成电路解决多标签分类问题的算法。通过数值模拟,为该算法的实施选择了最佳量子电路。以标准数据集为例演示了该算法的运行。实验结果与理论计算结果进行了比较。
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 去求助
来源期刊
Radiophysics and Quantum Electronics
Radiophysics and Quantum Electronics ENGINEERING, ELECTRICAL & ELECTRONIC-PHYSICS, APPLIED
CiteScore
1.10
自引率
12.50%
发文量
60
审稿时长
6-12 weeks
期刊介绍: Radiophysics and Quantum Electronics contains the most recent and best Russian research on topics such as: Radio astronomy; Plasma astrophysics; Ionospheric, atmospheric and oceanic physics; Radiowave propagation; Quantum radiophysics; Pphysics of oscillations and waves; Physics of plasmas; Statistical radiophysics; Electrodynamics; Vacuum and plasma electronics; Acoustics; Solid-state electronics. Radiophysics and Quantum Electronics is a translation of the Russian journal Izvestiya VUZ. Radiofizika, published by the Radiophysical Research Institute and N.I. Lobachevsky State University at Nizhnii Novgorod, Russia. The Russian volume-year is published in English beginning in April. All articles are peer-reviewed.
期刊最新文献
Scalable Quantum Processor Based on Superconducting Fluxonium Qubits Integrated Circuits for Quantum Machine Learning Based on Superconducting Artificial Atoms and Methods of Their Control Waveguide Integrated Superconducting Single-Photon Detector For Photonic And Ion Quantum Processors And Neuromorphic Computing The Influence of Cyclic Deformation on Elastic and Acoustic Properties of Chromium-Nickel Steels Development of a Microwave Diagnostic Method for Measurements of the Free-Surface Velocity in the Plane-Wave Experiment
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1