|Table of Contents|

Bayesian Binary Classification Algorithm Based on Quantum Counting(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2021年04期
Page:
117-121
Research Field:
·计算机科学与技术·
Publishing date:

Info

Title:
Bayesian Binary Classification Algorithm Based on Quantum Counting
Author(s):
Lu Chunyue; Guo Gongde; Lin Song
College of Computer and Cyber Security,Fujian Normal University,Fuzhou 350117,China
Keywords:
quantum machine learning; Bayesian classification; binary classification; quantum counting; phase estimation
PACS:
TP38 TP181
DOI:
10.3969/j.issn.1001-4616.2021.04.015
Abstract:
Bayesian classification algorithm is a supervised learning algorithm based on the statistics theory of probability,which is often used in classification problems. In this paper,a new quantum Bayesian classification algorithm is proposed by combining quantum counting with classical Bayesian classification algorithm. The required quantum states are prepared by a quantum random access memory,the oracle is used to phase flip and construct the corresponding operator,the quantum states are redescribed on the eigenstate space of the operator,and phase estimation is performed with the help of auxiliary particles. Then,the data required for Bayesian classification can be efficiently calculated after projection measurements and the quantum Bayesian classification algorithm can be realized. Compared with the classical algorithm,this algorithm has exponential acceleration in the low dimensional feature space.

References:

[1] 杨双波,韦栋. 周期受击简谐振子系统的经典与量子动力学[J]. 南京师大学报(自然科学版),2011,34(4):49-54.
[2]SHOR P W. Algorithms for quantum computation:discrete logarithms and factoring[C]//Proceedings 35th Annual Symposium on Foundations of Computer Science,Santa Fe,NM,USA. Piscataway:IEEE,1994:124-134.
[3]GROVER L K. Quantum mechanics helps in searching for a needle in a haystack[J]. Physical review letters,1997,79(2):325-328.
[4]WIEBE N,KAPOOR A,SVORE K M. Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning[J]. Quantum information & computation,2014,15(3):318-358.
[5]陈汉武,高越,张军. 量子K-近邻算法[J]. 东南大学学报(自然科学版),2015,45(4):647-651.
[6]LU S,BRAUNSTEIN S L. Quantum decision tree classifier[J]. Quantum information processing,2014,13(3):757-770.
[7]REBENTROST P,MOHSENI M,LLOYD S. Quantum support vector machine for big data classification[J]. Physical review letters,2014,113(13):130503-130508.
[8]BISHWAS A K,MANI A,PALADE V. An all-pair quantum SVM approach for big data multiclass classification[J]. Quantum information processing,2018,17(10):1-16.
[9]YU C H,GAO F,WANG Q L,et al. Quantum algorithm for association rules mining[J]. Physical review A,2016,94(4):042311-042319.
[10]吴嵘,张姣玲,刘小兰. 结合变异机制和量子PSO的关联规则挖掘算法[J]. 山东科技大学学报(自然科学版),2020,39(2):95-104.
[11]SHAO C P. Quantum speedup of bayes’ classifiers[J]. Journal of physics A:mathematical and theoretical,2020,53(4):045301-045328.
[12]邵晓根,鞠训光,胡局新,等. 基于改进权重的贝叶斯推理和TFIDF算法文本主题词提取研究[J]. 南京师大学报(自然科学版),2014,37(1):57-60,65.
[13]汤胜道,殷世茂. 正态分布下参数的模糊贝叶斯估计(英文)[J]. 南京师大学报(自然科学版),2015,38(1):13-20.
[14]张永军,刘金岭. 一种改进的高效贝叶斯短信文本分类器[J]. 南京师范大学学报(工程技术版),2014,14(3):70-74.
[15]金文梁. 量子搜索算法的多相位关系研究[J]. 计算机学报,2012,35(7):1440-1447.
[16]GIOVANNETTI V,LLOYD S,MACCONE L. Quantum random access memory[J]. Physical review letters,2008,100(16):160501-160506.
[17]张焕国,毛少武,吴万青,等. 量子计算复杂性理论综述[J]. 计算机学报,2016,39(12):2403-2428.
[18]BRASSARD G,HOYER P,MOSCA M,et al. Quantum amplitude amplification and estimation[J]. Contemporary mathematics,2002,305:53-74.

Memo

Memo:
-
Last Update: 2021-12-15