.png){kind=icon}
返回
Analog-Quantum Feature Mapping for Machine-Learning Applications
.png){kind=icon}
OA
AI
DOI：10.1103/PhysRevApplied.14.034034.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Quantum information processing is likely to have a far-reaching impact in the field of artificial intelligence. Noisy, intermediate-scale quantum devices provide a platform for exploring the possibility of attaining a quantum advantage through hybrid quantum-classical machine-learning algorithms. One example of such a hybrid algorithm is quantum kitchen sinks, which builds upon a classical algorithm known as random kitchen sinks to leverage a gate model quantum computer for machine-learning applications. We propose an alternative algorithm called analog-quantum kitchen sinks (AQKSs), which employs an analog-quantum computer for mapping data features into new features in a nonlinear manner. The new features can then be used by a classical algorithm to perform machine-learning tasks. We show the effectiveness of our algorithm for performing binary classification on both a synthetic dataset and a real-world dataset by simulating the operations of a quantum annealer. We demonstrate that the AQKS algorithm reduces the classification error of a linear classifier from 50% to 0.6% for the synthetic dataset and from 4.4% to 1.6% for the other dataset. Our proposed AQKS algorithm presents the possibility to use current quantum annealers for solving practical machine-learning problems.
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.4
论文数:
7.1K
被引数:
2.8W
机构
引用论文
A GRAPHICAL TECHNIQUE FOR DETERMINING EVAPOTRANSPIRATION BY THE THORNTHWAITE METHOD通过THORNTHWAITE方法确定蒸散量的图形技术
OAAI
.png){kind=icon}
OAAI分享收藏分享收藏Efficient DNA Cleavage Induced by Copper(II) Complexes of Hydrolysis Derivatives of 2,4,6‐Tri(2‐pyridyl)‐1,3,5‐triazine in the Presence of Reducing Agents在还原剂存在下，2,4，6-三 (2-吡啶基)-1,3，5-三嗪的水解衍生物的铜 (II) 配合物诱导的有效DNA裂解
PREAI
分享收藏分享收藏分享收藏
查看更多内容