A degressive quantum convolutional neural network for quantum state classification and code recognition
Qingshan Wu,
Wenjie Liu,
Yong Huang,
Haoyang Liu,
Hao Xiao,
Zixian Li
Affiliations
Qingshan Wu
School of Software, Nanjing University of Information Science and Technology, No. 219, Ning Liu Road, Nanjing, Jiangsu 210044, China
Wenjie Liu
School of Software, Nanjing University of Information Science and Technology, No. 219, Ning Liu Road, Nanjing, Jiangsu 210044, China; Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, No. 219, Ning Liu Road, Nanjing, Jiangsu 210044, China; Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services, Nanjing University of Information Science and Technology, No. 219, Ning Liu Road, Nanjing, Jiangsu 210044, China; Corresponding author
Yong Huang
Anhui Province Key Laboratory of Atmospheric Sciences and Satellite Remote Sensing, Anhui Institute of Meteorological Sciences, No.16, Shi He Road, Hefei, Anhui 230002, China
Haoyang Liu
School of Software, Nanjing University of Information Science and Technology, No. 219, Ning Liu Road, Nanjing, Jiangsu 210044, China
Hao Xiao
School of Information Engineering, Huzhou University, No.759, Second Ring East Road, Huzhou, Zhejiang 313002, China
Zixian Li
School of Software, Nanjing University of Information Science and Technology, No. 219, Ning Liu Road, Nanjing, Jiangsu 210044, China
Summary: With the rapid development of quantum computing, a variety of quantum convolutional neural networks (QCNNs) are proposed. However, only 1/2n2 features of an n-qubits input are transferred to the next layer in a quantum pooling layer, which results in the accuracy reduction. To solve this problem, a QCNN with a degressive circuit is proposed. In order to enhance the ability of extracting global features, we remove the parameters sharing strategy in the quantum convolutional layer and design a quantum convolutional kernel with global eyesight. In addition, to prevent a sharp feature reduction, a degressive parameterized quantum circuit is adopted to construct the pooling layer. Then the Z-basis measurement is only performed on the first qubit to control the operations on other qubits. Compared with the state-of-the-art QCNN, i.e., hybrid quantum-classical convolutional neural network, the accuracy of our model increased by 0.9%, 1%, and 3%, respectively, in three tasks: quantum state classification, binary code recognition, and quaternary code recognition.
