典型文献
Realizing number recognition with simulated quantum semi-restricted Boltzmann machine
文献摘要：
Quantum machine learning based on quantum algorithms may achieve an exponential speedup over classical algorithms in dealing with some problems such as clustering.In this paper,we use the method of training the lower bound of the average log likelihood function on the quantum Boltzmann machine(QBM)to recognize the handwritten number datasets and compare the training results with classical models.We find that,when the QBM is semi-restricted,the training results get better with fewer computing resources.This shows that it is necessary to design a targeted algorithm to speed up computation and save resources.
文献关键词：
中图分类号：
作者姓名：
Fuwen Zhang;Yonggang Tan;Qing-yu Cai
作者机构：
School of Physics,Zhengzhou University,Zhengzhou 450001,China;Innovation Academy for Precision Measurement Science and Technology,Chinese Academy of Sciences,Wuhan 430071,China;School of Physics and Electronic Information,Luoyang Normal University,Luoyang 471934,China;Center for Theoretical physics,Hainan University,Haikou 570228,China;School of Information and Communication Engineering,Hainan University,Haikou 570228,China
文献出处：
引用格式：
[1]Fuwen Zhang;Yonggang Tan;Qing-yu Cai-.Realizing number recognition with simulated quantum semi-restricted Boltzmann machine)[J].理论物理,2022(09):29-34
A类：
QBM
B类：
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AB值：
0.591544
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