Abstract: The rapid advancement of robotics in data-intensive domains raises critical challenges in ensuring both computational adaptability and security in the post-quantum era. Existing systems lack integrated approaches that simultaneously address quantum-resilient security, robust decision-making, and scalable software-driven modeling. This study proposes a unified framework that combines post-quantum cryptography with topological modeling to enhance the security, intelligence, and adaptability of big data robotics. Conceptually, the framework is built upon topological reasoning for environment representation, post-quantum algorithms for data protection, and MATLAB-Simulink as the core simulation and optimization platform. Methodologically, a layered model is developed, synthesizing control-theoretic AI safety, statistical learning, and multi-agent optimization into a software-driven architecture. The evaluation demonstrates that post-quantum cryptographic integration enhances system resilience against future quantum threats, while topological modeling improves adaptive reasoning under uncertainty. The results indicate that the proposed framework enables robotics systems to operate securely and efficiently in adversarial and data-rich environments. The implications are significant for advancing secure intelligent robotics, providing both a theoretical foundation and a practical pathway for autonomous systems in high-stakes domains such as healthcare, manufacturing, and defense. Keywords Secure intelligent systems, post-quantum cryptography, topological modeling, big data robotics, adaptive autonomy, AI safety, control theory, MATLAB-Simulink, quantum-AI integration, autonomous systems
Murali Krishna Pasupuleti (Mon,) studied this question.