基于参数化量子线路的量子神经网络数据分类  

Parameterized Quantum Circuits Based Quantum Neural Networks for Data Classification

作　　者：陈超 闫文杰 薛桂香 CHEN Chao;YAN Wenjie;XUE Guixiang(School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401,China)

机构地区：[1]河北工业大学人工智能与数据科学学院,天津300401

出　　处：《计算机科学》2024年第S02期577-583,共7页Computer Science

基　　金：国家自然科学基金(61702157)。

摘　　要：量子神经网络结合了量子计算与经典神经网络模型的优势,为未来人工智能领域的发展提供了一种全新的思路。尽管量子神经网络已被广泛研究,但数据编码方式以及不同训练线路对模型性能的影响尚未得到充分研究。为此,提出一种面向数据分类的量子神经网络新模型,该模型探究了不同数据编码方式和不同结构训练层对分类任务的影响。该方法首先对经典图像进行预处理,采用不同的数据编码方式将其编码到不同的参数化量子线路中进行训练,对模型的输出进行测量,使用参数移位规则更新训练参数完成数据分类。在MNIST手写体数据集上的实验结果表明,所提出的模型在数字{3,6}分类任务上的分类准确率超过了97%。与目前的主流方法相比,所提方法在分类准确率上有明显的提升。Quantum neural network combines the advantages of quantum computing and classical neural network model,and provides a new idea for the development of artificial intelligence in the future.Although quantum neural networks have been widely studied,the impact of data encoding methods and different training circuits on model performance has not yet been fully explored.Therefore,this paper proposes a new quantum neural network model for data classification,which explores the influence of diffe-rent data encoding methods and different structure training layers on classification tasks.The method first preprocesses the classical image,uses different data encoding methods to encode it into different parameterized quantum circuits for training,measures the output of the model,and uses the parameter shift rule to update the training parameters to complete the data classification.Experimental results on MNIST handwritten dataset show that the proposed model achieves more than 97% classification accuracy on digit{3,6}classification task.Compared with the current mainstream methods,the proposed method has a significant improvement in classification accuracy.

关 键 词：量子神经网络 量子计算 数据分类 量子线路 参数移位规则 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]