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A Degressive Quantum Convolutional Neural Network and for Quantum State Classification and Code Recognition

Qingshan Wu, Wenjie Liu, Haoyang Liu

Published Jan 1, 2023• 0 citations
Publisher
DOI
10.2139/ssrn.4638956

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With the rapid development of quantum computing, a variety of quantum convolutional neural networks (QCNNs) are proposed. However, only 1 / 2 n 2 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.
Traffic sign recognition is essential for autonomous driving, enhancing driving efficiency and ensuring road safety. Traffic signs utilize colors and patterns to relay critical information, with color accuracy being especially significant. Traditional models, reliant on extensive data and computing resources, struggle to meet the efficiency demands of electric vehicles with carbon-intelligent computing. Leveraging the physical properties of quantum superposition states, quantum computing offers a solution with its unique parallel computing capabilities, potentially enhancing recognition efficiency and facilitating real-time processing. Quantum convolutional neural networks (QCNNs) show promise in processing large-scale image data with improved efficiency and accuracy. However, most of QCNN researches focus on grayscale image classification, with limited studies on multichannel data. This article introduces the quantum channel attention convolutional neural network (QCACNN), which incorporates a quantum channel attention layer (QCAL) to enhance multichannel data classification. The experimental results demonstrate that QCACNNs surpasses traditional QCNNs in traffic sign recognition, performing comparably to conventional CNNs and SENet models with fewer computing resources. Detailed performance analysis and ablation studies validate the effectiveness of each component within this architecture. Quantum noise resistance tests confirm the robustness of QCACNN, making it a viable solution for electric vehicles with enhanced scalability.
Face recognition has become an essential biometric technology for applications such as security monitoring, user authentication, and intelligent surveillance systems. Conventional approaches based on deep neural networks, particularly convolution-based architectures, provide high recognition performance but often demand considerable computational resources and extended training durations as network complexity increases. To overcome these challenges, Quantum Machine Learning (QML) utilizes quantum computing concepts, including quantum superposition and entangled states, to achieve efficient feature representation and processing. This paper presents a Hybrid Quantum-Classical Face Recognition System built on a Multi-Gate Quantum Convolutional Neural Network (MG-QCNN). The proposed framework employs a 4-qubit variational quantum circuit that incorporates RY state encoding together with RX, RZ, and CNOT quantum operations to extract discriminative facial representations. In addition, a structural ablation analysis is conducted to investigate the influence of individual quantum components on the overall recognition capability of the model. To enhance spatial feature learning and classification performance, several classical neural network layers, namely convolution, batch normalization, max-pooling, dropout, and fully connected layers, are integrated after the quantum feature extraction stage. Within the hybrid framework, the quantum circuit is responsible for generating meaningful feature representations, whereas the classical neural network performs feature refinement and final identity classification. The framework is further combined with the DeepFace verification approach to perform facial similarity analysis and identity verification. Moreover, an Open-Set Face Recognition mechanism is incorporated to determine if an input facial image corresponds to a registered identity or represents an unseen individual. This capability increases system reliability and supports accurate unknown-person identification