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

被引：27
|
作者：
Ablayev, Farid [2 ]
Ablayev, Marat [2 ]
Huang, Joshua Zhexue [1 ]
Khadiev, Kamil [2 ]
Salikhova, Nailya [2 ]
Wu, Dingming [1 ]
机构：
[1] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518000, Peoples R China
[2] Kazan Fed Univ, Kazan 42008, Russia
来源：
BIG DATA MINING AND ANALYTICS | 2020年 / 3卷 / 01期
基金：
俄罗斯科学基金会;
关键词：
quantum algorithm; quantum programming; machine learning; POLYNOMIAL-TIME ALGORITHMS; DISCRETE LOGARITHMS; PRIME FACTORIZATION;
D O I：
10.26599/BDMA.2019.9020016
中图分类号：
TP18 [人工智能理论];
学科分类号：
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
收藏
页码：41 / 55
页数：15
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