%0 Journal Article
%A 李琴
%A 穆帅
%A 石金晶
%A 王雯萱
%A 肖子萌
%T Quantum classifier with parameterized quantum circuit based on the isolated quantum system
%D 2022
%R 10.19682/j.cnki.1005-8885.2022.2016
%J 中国邮电高校学报（英文）
%P 21-31
%V 29
%N 4
%X It is a critical challenge for quantum machine learning to classify the datasets accurately. This article develops a quantum classifier based on the isolated quantum system (QC-IQS) to classify nonlinear and multidimensional datasets. First, a model of QC-IQS is presented by creating parameterized quantum circuits (PQCs) based on the decomposing of unitary operators with the Hamiltonian in the isolated quantum system. Then, a parameterized quantum classification algorithm (QCA) is designed to calculate the classification results by updating the loss function until it converges. Finally, the experiments on nonlinear random number datasets and Iris datasets are designed to demonstrate that the QC-IQS model can handle and generate accurate classification results on different kinds of datasets. The experimental results reveal that the QC-IQS is adaptive and learnable to handle different types of data. Moreover, QC-IQS compensates the issue that the accuracy of previous quantum classifiers declines when dealing with diverse datasets. It promotes the process of novel data processing with quantum machine learning and has the potential for more comprehensive applications in the future.<br>
%U https://jcupt.bupt.edu.cn/CN/10.19682/j.cnki.1005-8885.2022.2016