As quantum computing continues to advance, the development of quantum-secure neural networks is crucial to prevent adversarial attacks. This chapter proposes three principles for quantum-secure design: (1) the use of post-quantum cryptography, (2) the implementation of quantum-resistant neural network architectures, and (3) the assurance of transparent and accountable development and deployment. These principles are supported by various quantum strategies, including quantum data anonymization, quantum-resistant neural networks, and quantum encryption. The chapter also identifies open issues in quantum security, privacy, and trust and recommends exploring adaptive adversarial attacks and auto-adversarial attacks as future directions. The proposed design principles and recommendations provide guidance for the development of quantum-secure neural networks, ensuring the integrity and reliability of machine learning models in the quantum era.