Quantum neural networks have made significant progress in classification tasks; however, they face challenges in network intrusion detection, especially parameter overhead and deployment on resource-constrained edge devices. In this paper, we propose a simplified hybrid quantum-classical neural network. The model combines quantum processing with classical preprocessing to address these challenges. The quantum component uses six quantum processing units with circuit depth two to extract features from compressed network traffic data. Classical layers handle dimensionality reduction and output classification. We evaluate the model on three benchmark intrusion detection datasets representing legacy, challenging, and modern attack scenarios. Experimental results demonstrate the effectiveness of the proposed approach. The model achieves accuracies of 97.37%, 89.95%, and 99.57% on the three benchmarks respectively. It uses only 24,000 trainable parameters compared to 230,000 parameters in classical baselines. This represents a statistically significant 90% parameter reduction with only 0.68% point average accuracy loss on comparable datasets. However, evaluation was conducted on 10% data subsets using quantum simulators rather than actual quantum hardware, which represents the primary limitation for real-world deployment.