MNIST数据库
计算机科学
卷积神经网络
量子电路
参数化复杂度
量子
人工神经网络
上下文图像分类
特征(语言学)
人工智能
模式识别(心理学)
量子门
算法
二元分类
量子算法
深度学习
量子计算机
量子网络
图像(数学)
支持向量机
物理
量子力学
语言学
哲学
作者
Shuang Wang,Ke-Han Wang,Tao Cheng,Run-Sheng Zhao,Hongyang Ma,Shuai Guo
标识
DOI:10.1088/1674-1056/ad342e
摘要
We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantum circuit, thereby propose a novel hybrid quantum deep neural network (HQDNN) used for image classification. After bilinear interpolation reduces the original image to a suitable size, an improved novel enhanced quantum representation (INEQR) is used to encode it into quantum states as the input of the HQDNN. Multi-layer parameterized quantum circuits are used as the main structure to implement feature extraction and classification. The output results of parameterized quantum circuits are converted into classical data through quantum measurements and then optimized on a classical computer. To verify the performance of the HQDNN, we conduct binary classification and three classification experiments on the MNIST (Modified National Institute of Standards and Technology) data set. In the first binary classification, the accuracy of 0 and 4 exceeds 98%. Then we compare the performance of three classification with other algorithms, the results on two datasets show that the classification accuracy is higher than that of quantum deep neural network and general quantum convolutional neural network.
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