检索结果-南通市图书馆

Recent advances for quantum classifiers

Science China(Physics,Mechanics & Astronomy) 2022年第2期65卷 1-23页

作者： Weikang Li Dong-Ling Deng Center for quantum Information Institute for Interdisciplinary Information ScienceTsinghua UniversityBeijing 100084China Shanghai Qi Zhi Institute Shanghai 200232China

Machine learning has achieved dramatic success in a broad spectrum of *** interplay with quantum physics may lead to unprecedented perspectives for both fundamental research and commercial applications,giving rise to an emergent research frontier of quantum machine *** this line,quantum classifiers,which are quantum devices that aim to solve classification problems in machine learning,have attracted tremendous attention *** this review,we give a relatively comprehensive overview for the studies of quantum classifiers,with a focus on recent ***,we will review a number of quantum classification algorithms,including quantum support vector machines,quantum kernel methods,quantum decision tree classifiers,quantum nearest neighbor algorithms,and quantum annealing based ***,we move on to introduce the variational quantum classifiers,which are essentially variational quantum circuits for *** will review different architectures for constructing variational quantum classifiers and introduce the barren plateau problem,where the training of quantum classifiers might be hindered by the exponentially vanishing *** addition,the vulnerability aspect of quantum classifiers in the setting of adversarial learning and the recent experimental progress on different quantum classifiers will also be discussed.

关键词： quantum machine learning quantum classifiers quantum kernel methods variational quantum algorithms

来源：

维普期刊数据库

同方期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Universal adversarial examples and perturbations for quantum classifiers

引用

National Science Review 2022年第6期9卷 48-55页

作者： Weiyuan Gong Dong-Ling Deng Center for quantum Information Institute for Interdisciplinary Information Sciences (ⅢS) Tsinghua University Shanghai Qi Zhi Institute

quantum machine learning explores the interplay between machine learning and quantum physics, which may lead to unprecedented perspectives for both fields. In fact, recent works have shown strong evidence that quantum computers could outperform classical computers in solving certain notable machine learning tasks. Yet, quantum learning systems may also suffer from the vulnerability problem: adding a tiny carefully crafted perturbation to the legitimate input data would cause the systems to make incorrect predictions at a notably high confidence level. In this paper, we study the universality of adversarial examples and perturbations for quantum classifiers. Through concrete examples involving classifications of real-life images and quantum phases of matter, we show that there exist universal adversarial examples that can fool a set of different quantum classifiers. We prove that, for a set of k classifiers with each receiving input data of n qubits, an O(ln [k]/2~n) increase of the perturbation strength is enough to ensure a moderate universal adversarial risk. In addition, for a given quantum classifier, we show that there exist universal adversarial perturbations, which can be added to different legitimate samples to make them adversarial examples for the classifier. Our results reveal the universality perspective of adversarial attacks for quantum machine learning systems, which would be crucial for practical applications of both near-term and future quantum technologies in solving machine learning problems.

关键词： quantum machine learning quantum classifiers adversarial examples measure concentration quantum no-free-lunch theorem

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：