Quantum Algorithm on Logistic Regression Problem

被引：0

作者：

Kim, Jun Suk ^{[1
]}

Ahn, Chang Wook ^{[1
]}

机构：

[1] Gwangju Inst Sci & Technol, Sch Elect Engn & Comp Sci, Gwangju, South Korea

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2019年 / E102D卷 / 04期

关键词：

quantum machine learning; Deutsch-Jozsa Algorithm; logistic regression; feature selection;

D O I：

10.1587/transinf.2018EDL8223

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We examine the feasibility of Deutsch-Jozsa Algorithm, a basic quantum algorithm, on a machine learning-based logistic regression problem. Its major property to distinguish the function type with an exponential speedup can help identify the feature unsuitability much more quickly. Although strict conditions and restrictions to abide exist, we reconfirm the quantum superiority in many aspects of modern computing.

引用

页码：856 / 858

页数：3

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