As quantum computing continues to advance, the development of quantum-secure neural networks is crucial to prevent adversarial attacks. This paper proposes three quantum-secure design principles: (1) using post-quantum cryptography, (2) employing quantum-resistant neural network architectures, and (3) ensuring 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 paper 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 developing quantum-secure neural networks, ensuring the integrity and reliability of machine learning models in the quantum era.

翻译：随着量子计算的持续发展，开发量子安全的神经网络对于防范对抗性攻击至关重要。本文提出了三条量子安全设计原则：(1) 使用后量子密码学，(2) 采用抗量子神经网络架构，(3) 确保开发与部署过程的透明性与可问责性。这些原则得到了多种量子策略的支持，包括量子数据匿名化、抗量子神经网络以及量子加密。本文还指出了量子安全、隐私与信任领域存在的开放性问题，并建议将探索自适应对抗攻击与自动对抗攻击作为未来的研究方向。所提出的设计原则与建议为开发量子安全的神经网络提供了指导，以确保量子时代机器学习模型的完整性与可靠性。