增强数据编码的量子自动编码器

Carlos Bravo-Prieto
{"title":"增强数据编码的量子自动编码器","authors":"Carlos Bravo-Prieto","doi":"10.1088/2632-2153/ac0616","DOIUrl":null,"url":null,"abstract":"We present the enhanced feature quantum autoencoder, or EF-QAE, a variational quantum algorithm capable of compressing quantum states of different models with higher fidelity. The key idea of the algorithm is to define a parameterized quantum circuit that depends upon adjustable parameters and a feature vector that characterizes such a model. We assess the validity of the method in simulations by compressing ground states of the Ising model and classical handwritten digits. The results show that EF-QAE improves the performance compared to the standard quantum autoencoder using the same amount of quantum resources, but at the expense of additional classical optimization. Therefore, EF-QAE makes the task of compressing quantum information better suited to be implemented in near-term quantum devices.","PeriodicalId":18148,"journal":{"name":"Mach. Learn. Sci. Technol.","volume":"86 1","pages":"35028"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":"{\"title\":\"Quantum autoencoders with enhanced data encoding\",\"authors\":\"Carlos Bravo-Prieto\",\"doi\":\"10.1088/2632-2153/ac0616\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present the enhanced feature quantum autoencoder, or EF-QAE, a variational quantum algorithm capable of compressing quantum states of different models with higher fidelity. The key idea of the algorithm is to define a parameterized quantum circuit that depends upon adjustable parameters and a feature vector that characterizes such a model. We assess the validity of the method in simulations by compressing ground states of the Ising model and classical handwritten digits. The results show that EF-QAE improves the performance compared to the standard quantum autoencoder using the same amount of quantum resources, but at the expense of additional classical optimization. Therefore, EF-QAE makes the task of compressing quantum information better suited to be implemented in near-term quantum devices.\",\"PeriodicalId\":18148,\"journal\":{\"name\":\"Mach. Learn. Sci. Technol.\",\"volume\":\"86 1\",\"pages\":\"35028\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"30\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mach. Learn. Sci. Technol.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1088/2632-2153/ac0616\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mach. Learn. Sci. Technol.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/2632-2153/ac0616","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 30

摘要

我们提出了增强型特征量子自编码器(enhanced feature quantum autoencoder, EF-QAE),这是一种变分量子算法,能够以更高的保真度压缩不同模型的量子态。该算法的关键思想是定义一个参数化的量子电路,该电路依赖于可调参数和表征该模型的特征向量。我们通过压缩伊辛模型和经典手写数字的基态来评估该方法在模拟中的有效性。结果表明,在使用相同量子资源的情况下,EF-QAE比标准量子自编码器的性能有所提高,但代价是额外的经典优化。因此,EF-QAE使得压缩量子信息的任务更适合在近期量子器件中实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantum autoencoders with enhanced data encoding
We present the enhanced feature quantum autoencoder, or EF-QAE, a variational quantum algorithm capable of compressing quantum states of different models with higher fidelity. The key idea of the algorithm is to define a parameterized quantum circuit that depends upon adjustable parameters and a feature vector that characterizes such a model. We assess the validity of the method in simulations by compressing ground states of the Ising model and classical handwritten digits. The results show that EF-QAE improves the performance compared to the standard quantum autoencoder using the same amount of quantum resources, but at the expense of additional classical optimization. Therefore, EF-QAE makes the task of compressing quantum information better suited to be implemented in near-term quantum devices.
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
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