Cyber-Physical Systems (CPS) have been ingrained in many critical infrastructures, including power generation and distribution, transportation, and healthcare. However, their interconnectedness also leaves them open to cyber perils such as breaches and system malfunctions. The available defence techniques are mainly grounded on conventional machine learning techniques and fail to offer comprehensive robustness, privacy and computational scalability in real-world CPS. On the other hand, Quantum Machine Learning (QML) brings a potential computation benefit concerning classical ML, but its implications for CPS security are largely unknown. In this paper, we suggest a Hybrid Secure Neural and Quantum Framework that leverages both secure neural computing approaches and quantum-enhanced machine learning methods to enhance the security of intelligent CPS. Our approach uses adversarially robust deep learning, privacy-preserving learning techniques, and quantum-inspired machine learning to classify and cryptographic methods to construct secure learning to offer greater protection against advanced adversaries. Extensive experiments on classical CPS benchmark datasets show that the proposed hybrid model can outperform the baselines in overall robustness against adversaries, AP resilience and computational efficiency. This work paves the way for the future of secure and quantum-enhanced innovative CPS architectures.