Abstract:

Integrating quantum computing with AI can dramatically improve the future of data encryption and security in cyberspaces. With growing interaction of users in immersive worlds like the metaverse, user data along with virtual assets require more protection. Existing security approaches, such as classical encryption and rule-based intrusion detection systems, fall short of sophisticated mitigation techniques for cyber threats and quantum-based attacks. To solve these disadvantages, we introduce a new Hybrid framework called Quantum-AI Secured Virtual Framework (QASVF) which integrates AI-powered anomaly detection with data security and virtualized environment frameworks using quantum cryptographic protocols. QASVF employs QKD for eavesdropping, while employing attack inspection through reinforcement learning and deep neural networks. The proposed method is developed to provide secure communication networks, perform user authentication through quantum-strengthened biometrics, and continuously adapt to new vulnerabilities using AI-driven learning cycles. In conjunction, these procedures drastically decrease the probability of data breaches, identity theft, and unauthorized access to virtual worlds. Implementation results of QASVF show improved threat detection performance, lower latency in secure transaction processing, and increased resilience to quantum decryption attacks. The hybrid integration has enabled a new trust elastic model of security which can be considered the next generation of security and trust for virtual relations.