On Quantum Methods for Machine Learning Problems Part Ⅰ: Quantum Tools

被引：0

作者：

Farid Ablayev ^{[1
]}

Marat Ablayev ^{[1
]}

Joshua Zhexue Huang ^{[2
]}

Kamil Khadiev ^{[1
]}

Nailya Salikhova ^{[1
]}

Dingming Wu ^{[2
]}

机构：

[1] the Kazan Federal University

[2] the College of Computer Science & Software Engineering, Shenzhen University

来源：

Big Data Mining and Analytics | 2020年 / 01期

基金：

俄罗斯科学基金会;

关键词：

quantum algorithm; quantum programming; machine learning;

D O I：

暂无

中图分类号：

O413 [量子论]; TP181 [自动推理、机器学习];

学科分类号：

070201 ; 081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This is a review of quantum methods for machine learning problems that consists of two parts. The first part, "quantum tools", presents the fundamentals of qubits, quantum registers, and quantum states, introduces important quantum tools based on known quantum search algorithms and SWAP-test, and discusses the basic quantum procedures used for quantum search methods. The second part, "quantum classification algorithms",introduces several classification problems that can be accelerated by using quantum subroutines and discusses the quantum methods used for classification.

引用

页码：41 / 55

页数：15

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