Multi-Path Parallel Quantum Convolutional Neural Network
Han Qi, Xintong Huo, Abdullah Gani
Quantum Convolutional Neural Networks (QCNN) migrate the convolution and pooling concepts of classical Convolutional Neural Networks (CNN) to variational quantum circuits, showing potential on Noisy Intermediate-Scale Quantum (NISQ) devices. However, traditional QCNN adopts a single-depth circuit structure, which has limitations in multi-scale feature extraction, and is prone to the barren plateau phenomenon during training, making it difficult to adapt to high-dimensional real-world scenarios such as network intrusion detection. This paper proposes a QCNN architecture based on a multi-path parallel structure. For the network intrusion detection task, it draws on the Inception module to construct three parallel quantum convolution paths: shallow, medium, and deep, achieving hierarchical extraction of multi-scale features; residual connections are introduced to preserve encoded state information and provide gradient shortcuts; a hybrid optimization strategy combining classical Adam and Quantum Natural Gradient (QNG) is adopted to adapt to the heterogeneous parameter space. Experimental validation shows that, on the UNSW-NB15 network intrusion detection dataset, the proposed Multi-Path Parallel Quantum Convolutional Neural Network (MP-QCNN) improves detection accuracy by 3% compared to traditional single-path QCNN and effectively mitigates the barren plateau phenomenon. This design strictly maintains NISQ depth constraints and quantum parallelism advantages, providing an efficient and stable feature extraction framework for high-dimensional tasks such as network intrusion detection.
International Conference on Multi-scale Artificial Intelligence (MAI)•Apr 24, 2026•0 citations