加速格罗弗自适应搜索:采用更高阶公式的 Qubit 和门数减少策略

Yuki Sano;Kosuke Mitarai;Naoki Yamamoto;Naoki Ishikawa
{"title":"加速格罗弗自适应搜索:采用更高阶公式的 Qubit 和门数减少策略","authors":"Yuki Sano;Kosuke Mitarai;Naoki Yamamoto;Naoki Ishikawa","doi":"10.1109/TQE.2024.3393437","DOIUrl":null,"url":null,"abstract":"Grover adaptive search (GAS) is a quantum exhaustive search algorithm designed to solve binary optimization problems. In this article, we propose higher order binary formulations that can simultaneously reduce the numbers of qubits and gates required for GAS. Specifically, we consider two novel strategies: one that reduces the number of gates through polynomial factorization, and the other that halves the order of the objective function, subsequently decreasing circuit runtime and implementation cost. Our analysis demonstrates that the proposed higher order formulations improve the convergence performance of GAS by reducing both the search space size and the number of quantum gates. Our strategies are also beneficial for general combinatorial optimization problems using one-hot encoding.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-12"},"PeriodicalIF":0.0000,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10508492","citationCount":"0","resultStr":"{\"title\":\"Accelerating Grover Adaptive Search: Qubit and Gate Count Reduction Strategies With Higher Order Formulations\",\"authors\":\"Yuki Sano;Kosuke Mitarai;Naoki Yamamoto;Naoki Ishikawa\",\"doi\":\"10.1109/TQE.2024.3393437\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Grover adaptive search (GAS) is a quantum exhaustive search algorithm designed to solve binary optimization problems. In this article, we propose higher order binary formulations that can simultaneously reduce the numbers of qubits and gates required for GAS. Specifically, we consider two novel strategies: one that reduces the number of gates through polynomial factorization, and the other that halves the order of the objective function, subsequently decreasing circuit runtime and implementation cost. Our analysis demonstrates that the proposed higher order formulations improve the convergence performance of GAS by reducing both the search space size and the number of quantum gates. Our strategies are also beneficial for general combinatorial optimization problems using one-hot encoding.\",\"PeriodicalId\":100644,\"journal\":{\"name\":\"IEEE Transactions on Quantum Engineering\",\"volume\":\"5 \",\"pages\":\"1-12\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10508492\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Quantum Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10508492/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Quantum Engineering","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10508492/","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

格罗弗自适应搜索(GAS)是一种量子穷举搜索算法,旨在解决二进制优化问题。在本文中,我们提出了能同时减少 GAS 所需的量子比特和门数量的高阶二进制公式。具体来说,我们考虑了两种新策略:一种是通过多项式因式分解减少门的数量,另一种是将目标函数的阶数减半,从而减少电路运行时间和实现成本。我们的分析表明,通过减少搜索空间大小和量子门数量,所提出的高阶公式改善了 GAS 的收敛性能。我们的策略也适用于使用单次编码的一般组合优化问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accelerating Grover Adaptive Search: Qubit and Gate Count Reduction Strategies With Higher Order Formulations
Grover adaptive search (GAS) is a quantum exhaustive search algorithm designed to solve binary optimization problems. In this article, we propose higher order binary formulations that can simultaneously reduce the numbers of qubits and gates required for GAS. Specifically, we consider two novel strategies: one that reduces the number of gates through polynomial factorization, and the other that halves the order of the objective function, subsequently decreasing circuit runtime and implementation cost. Our analysis demonstrates that the proposed higher order formulations improve the convergence performance of GAS by reducing both the search space size and the number of quantum gates. Our strategies are also beneficial for general combinatorial optimization problems using one-hot encoding.
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
CiteScore
8.00
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