Evolving a Multi-Population Evolutionary-QAOA on Distributed QPUs

Francesca Schiavello, Edoardo Altamura, Ivano Tavernelli, Stefano Mensa, Benjamin Symons
{"title":"Evolving a Multi-Population Evolutionary-QAOA on Distributed QPUs","authors":"Francesca Schiavello, Edoardo Altamura, Ivano Tavernelli, Stefano Mensa, Benjamin Symons","doi":"arxiv-2409.10739","DOIUrl":null,"url":null,"abstract":"Our research combines an Evolutionary Algorithm (EA) with a Quantum\nApproximate Optimization Algorithm (QAOA) to update the ansatz parameters, in\nplace of traditional gradient-based methods, and benchmark on the Max-Cut\nproblem. We demonstrate that our Evolutionary-QAOA (E-QAOA) pairing performs on\npar or better than a COBYLA-based QAOA in terms of solution accuracy and\nvariance, for $d$-3 regular graphs between 4 and 26 nodes, using both\n$max\\_count$ and Conditional Value at Risk (CVaR) for fitness function\nevaluations. Furthermore, we take our algorithm one step further and present a\nnovel approach by presenting a multi-population EA distributed on two QPUs,\nwhich evolves independent and isolated populations in parallel, classically\ncommunicating elite individuals. Experiments were conducted on both simulators\nand quantum hardware, and we investigated the relative performance accuracy and\nvariance.","PeriodicalId":501226,"journal":{"name":"arXiv - PHYS - Quantum Physics","volume":"36 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Quantum Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.10739","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Our research combines an Evolutionary Algorithm (EA) with a Quantum Approximate Optimization Algorithm (QAOA) to update the ansatz parameters, in place of traditional gradient-based methods, and benchmark on the Max-Cut problem. We demonstrate that our Evolutionary-QAOA (E-QAOA) pairing performs on par or better than a COBYLA-based QAOA in terms of solution accuracy and variance, for $d$-3 regular graphs between 4 and 26 nodes, using both $max\_count$ and Conditional Value at Risk (CVaR) for fitness function evaluations. Furthermore, we take our algorithm one step further and present a novel approach by presenting a multi-population EA distributed on two QPUs, which evolves independent and isolated populations in parallel, classically communicating elite individuals. Experiments were conducted on both simulators and quantum hardware, and we investigated the relative performance accuracy and variance.
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
在分布式 QPU 上演化多人群进化 QAOA
我们的研究结合了进化算法(EA)和量子近似优化算法(QAOA)来更新解析参数,取代了传统的基于梯度的方法,并对最大切割问题进行了基准测试。我们证明,对于 4 节点到 26 节点之间的 $d$-3 规则图,我们的进化-QAOA(E-QAOA)配对算法在求解精度和方差方面的表现与基于 COBYLA 的 QAOA 算法相当,甚至更好。此外,我们的算法更进一步,提出了一种新的方法,即在两个 QPU 上分布一个多种群 EA,并行演化独立和孤立的种群,经典地交流精英个体。我们在模拟器和量子硬件上进行了实验,并研究了相对的性能精度和方差。
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
自引率
0.00%
发文量
0
期刊最新文献
Performance advantage of protective quantum measurements Mechanical Wannier-Stark Ladder of Diamond Spin-Mechanical Lamb Wave Resonators Towards practical secure delegated quantum computing with semi-classical light Quantum-like nonlinear interferometry with frequency-engineered classical light QUBO-based SVM for credit card fraud detection on a real QPU
×
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