Edge computing plays a pivotal role in enabling ultra-low-latency and high-throughput applications such as real-time holography, extended reality (XR), brain-machine interfaces and autonomous systems within emerging 6G/7G networks. This chapter introduces Hybrid Quantum–Classical Neural Network (HQCNN) architectures as a transformative approach to achieving edge intelligence. By synergising classical neural layers for real-time responsiveness with the computational acceleration of quantum layers, HQCNNs offer scalable, resilient solutions for highly distributed systems. The integration of federated learning and quantum-aware deployment strategies further enhances data privacy and reduces communication overhead, crucial for mission-critical, latency-sensitive tasks. These hybrid architectures support context-aware inference, enabling rapid decision-making and efficient resource utilisation across heterogeneous edge environments. The chapter underscores the practical advantages of HQCNNs in future intelligent networks, emphasising their role in powering next-generation applications that demand both precision and speed. Through this framework, 6G/7G networks can achieve unprecedented levels of performance, reliability and edge-based cognitive automation.