Based on QUBO models with quantum-inspired algorithms to enhance the CVQKD systems to ensure security of hacking

This paper presents the need for innovative solutions to optimize computational power networks and the application of quantum computing to tackle real-world challenges. Our research addresses this critical issue by developing a robust pre-training scheme that integrates Quantum Unweighted Quadratic Unconstrained Binary Optimization (QUBO) models with quantum-inspired algorithms. This approach aims to enhance the adversarial robustness of CVQKD systems, ensuring their security in the face of sophisticated hacking attempts. Our experimental results demonstrate that the proposed strategy effectively defends against adversarial attacks while maintaining the integrity of secret keys, showcasing the adaptability and efficiency of QUBO models in quantum communication scenarios. This work not only contributes to the broader application of QUBO models in quantum communication but also provides a robust pre-training scheme that can be generalized and transplanted to other machine learning-assisted systems, significantly improving their security in the face of adversarial attacks.