在尺寸受限量子退火硬件上训练受限玻尔兹曼机的软件技术

IF 2.4 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Frontiers in Computer Science Pub Date : 2023-10-16 DOI:10.3389/fcomp.2023.1286591
Ilmo Salmenperä, Jukka K. Nurminen
{"title":"在尺寸受限量子退火硬件上训练受限玻尔兹曼机的软件技术","authors":"Ilmo Salmenperä, Jukka K. Nurminen","doi":"10.3389/fcomp.2023.1286591","DOIUrl":null,"url":null,"abstract":"Restricted Boltzmann machines are common machine learning models that can utilize quantum annealing devices in their training processes as quantum samplers. While this approach has shown promise as an alternative to classical sampling methods, the limitations of quantum annealing hardware, such as the number of qubits and the lack of connectivity between the qubits, still pose a barrier to wide-scale adoption. We propose the use of multiple software techniques such as dropout method, passive labeling, and parallelization techniques for addressing these hardware limitations. The study found that using these techniques along with quantum sampling showed comparable results to its classical counterparts in certain contexts, while in others the increased complexity of the sampling process hindered the performance of the trained models. This means that further research into the behavior of quantum sampling needs to be done to apply quantum annealing to training tasks of more complicated RBM models.","PeriodicalId":52823,"journal":{"name":"Frontiers in Computer Science","volume":"221 1","pages":"0"},"PeriodicalIF":2.4000,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Software techniques for training restricted Boltzmann machines on size-constrained quantum annealing hardware\",\"authors\":\"Ilmo Salmenperä, Jukka K. Nurminen\",\"doi\":\"10.3389/fcomp.2023.1286591\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Restricted Boltzmann machines are common machine learning models that can utilize quantum annealing devices in their training processes as quantum samplers. While this approach has shown promise as an alternative to classical sampling methods, the limitations of quantum annealing hardware, such as the number of qubits and the lack of connectivity between the qubits, still pose a barrier to wide-scale adoption. We propose the use of multiple software techniques such as dropout method, passive labeling, and parallelization techniques for addressing these hardware limitations. The study found that using these techniques along with quantum sampling showed comparable results to its classical counterparts in certain contexts, while in others the increased complexity of the sampling process hindered the performance of the trained models. This means that further research into the behavior of quantum sampling needs to be done to apply quantum annealing to training tasks of more complicated RBM models.\",\"PeriodicalId\":52823,\"journal\":{\"name\":\"Frontiers in Computer Science\",\"volume\":\"221 1\",\"pages\":\"0\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Computer Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/fcomp.2023.1286591\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fcomp.2023.1286591","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

受限玻尔兹曼机是一种常见的机器学习模型,它可以在训练过程中利用量子退火装置作为量子采样器。虽然这种方法已经显示出作为经典采样方法的替代方案的希望,但量子退火硬件的局限性,例如量子位的数量和量子位之间缺乏连接,仍然对大规模采用构成障碍。我们建议使用多种软件技术,如dropout方法,被动标记和并行化技术来解决这些硬件限制。研究发现,在某些情况下,使用这些技术和量子采样显示出与经典采样相当的结果,而在其他情况下,采样过程的复杂性增加阻碍了训练模型的性能。这意味着需要进一步研究量子采样的行为,以便将量子退火应用于更复杂的RBM模型的训练任务。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Software techniques for training restricted Boltzmann machines on size-constrained quantum annealing hardware
Restricted Boltzmann machines are common machine learning models that can utilize quantum annealing devices in their training processes as quantum samplers. While this approach has shown promise as an alternative to classical sampling methods, the limitations of quantum annealing hardware, such as the number of qubits and the lack of connectivity between the qubits, still pose a barrier to wide-scale adoption. We propose the use of multiple software techniques such as dropout method, passive labeling, and parallelization techniques for addressing these hardware limitations. The study found that using these techniques along with quantum sampling showed comparable results to its classical counterparts in certain contexts, while in others the increased complexity of the sampling process hindered the performance of the trained models. This means that further research into the behavior of quantum sampling needs to be done to apply quantum annealing to training tasks of more complicated RBM models.
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
Frontiers in Computer Science
Frontiers in Computer Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.30
自引率
0.00%
发文量
152
审稿时长
13 weeks
期刊最新文献
Quantum annealing research at CMU: algorithms, hardware, applications Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM) Lived experience in human-building interaction (HBI): an initial framework The impact of architectural form on physiological stress: a systematic review Care-full data, care-less systems: making sense of self-care technologies for mental health with humanistic practitioners in the United Kingdom
×
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