A survey on quantum classification and clustering algorithms

ZHAO Chao; NIU Wei-na; YANG Jun-chuang

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A survey on quantum classification and clustering algorithms

更新时间：2026-09-04

- A survey on quantum classification and clustering algorithms
- Vol. 37, Issue 8, Pages: 1-5(2020)
- 作者机构：
  
  1. 河北工程大学机构信息与电气工程学院,河北,邯郸,056038
  2. 四川大学网络空间安全学院,四川,成都,610207
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2020
- 稿件说明：
移动端阅览
赵超, 牛伟纳, 杨俊闯. 基于量子计算的分类和聚类算法综述[J]. 微电子学与计算机, 2020, 37(8): 1-5.

ZHAO Chao, NIU Wei-na, YANG Jun-chuang. A survey on quantum classification and clustering algorithms[J]. 2020, 37(8): 1-5.
赵超, 牛伟纳, 杨俊闯. 基于量子计算的分类和聚类算法综述[J]. 微电子学与计算机, 2020, 37(8): 1-5. DOI：

ZHAO Chao, NIU Wei-na, YANG Jun-chuang. A survey on quantum classification and clustering algorithms[J]. 2020, 37(8): 1-5. DOI：

摘要

越来越多的研究表明，借助量子计算技术可以提高有监督分类算法和无监督聚类算法的计算效率，甚至是学习精度.通常采用的方法有：基于量子理论将经典信息转换为量子态的形式存储起来，用量子态来表示所有样本；以量子态之间的距离替代样本数据之间的经典距离，形成新的相似度来度量样本数据间的相似性等.通过理论和模拟验证表明，量子计算可以实现对经典机器学习算法的加速.最后，总结了量子机器学习技术的优势和目前所存在的问题，并展望了未来该领域的发展趋势.

Abstract

A lot of work shows that quantum computing can speed up supervised and unsupervised learning algorithms

even improving the performance of these algorithms. Usually the following methods are used: based on quantum theory

it encodes classical information into quantum state

and then represents all of samples in dataset by one or several quantum states; It quantifies the similarity of two samples by measuring the distance between the quantum states through a quantum algorithm. The theoretical and simulation results show that quantum computation can accelerate classical machine learning algorithms. Finally

it summarizes the advantages of quantum machine learning technology and the existing problems

and looks forward to the future development trend of this field.

关键词

Keywords

references

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WIEBE N, KAPOOR A, SVORE K. Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning[J]. Quantum Information % Computation, 2014, 15(3):318-358. DOI: 318

CAI X D, WU D, SU Z E, et al. Entanglement-based machine learning on a quantum computer[J]. Physical Review Letters, 2014, 114(11):110504. DOI: 110504

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