Quantum computing leverages quantum phenomena such as superposition and entanglement to solve complex optimization and data analytics problems more efficiently than classical computing. Recent advances in quantum algorithms, including QAOA, Quantum Annealing, VQE, and hybrid quantum-classical models, have enabled applications in finance, healthcare, logistics, manufacturing, cybersecurity, and artificial intelligence. This paper surveys quantum computing applications, proposes a taxonomy, and presents a hybrid quantum-classical framework integrating quantum optimization with quantum-enhanced machine learning. Mathematical formulation, algorithmic representation, and experimental evaluation demonstrate improved optimization accuracy, computational efficiency, predictive performance, and solution quality over conventional approaches, highlighting quantum computing's potential for next-generation intelligent decision-making systems.
John McCarthy, M. Minsky· International Journal of Mod...· 0 citations
Intelligent robotics has transformed industrial automation, healthcare, logistics, autonomous transportation, agriculture, and service applications by enabling robots to perform complex tasks with minimal human intervention. However, conventional robotic systems rely on offline supervised learning models trained on static datasets, limiting their ability to adapt to dynamic environments, sensor variations, changing tasks, and unforeseen conditions. Frequent retraining increases computational cost, downtime, and catastrophic forgetting. Continual learning addresses these limitations by enabling robots to acquire new knowledge while preserving previously learned skills through adaptive memory management, knowledge consolidation, reinforcement learning, and dynamic neural architectures. This paper proposes a comprehensive continual learning framework that integrates adaptive knowledge representation, experience replay, task-aware optimization, reinforcement learning-based policy refinement, and dynamic parameter consolidation. The framework supports long-term knowledge retention, rapid adaptation, and stable sequential learning while mitigating catastrophic forgetting. Mathematical formulations for continual optimization, adaptive loss minimization, knowledge retention, and policy adaptation are also presented. Experimental evaluation using metrics such as adaptation accuracy, task completion rate, learning efficiency, knowledge retention, inference latency, computational overhead, energy consumption, and catastrophic forgetting demonstrates superior performance compared with conventional deep learning and reinforcement learning approaches. Furthermore, the framework supports scalable cloud-edge robotic ecosystems for collaborative learning and knowledge sharing, making it well suited for Industry 5.0 manufacturing, autonomous vehicles, intelligent warehouses, healthcare robotics, and smart city applications. Overall, the proposed framework establishes continual learning as a fundamental approach for achieving lifelong, adaptive, and intelligent robotic systems.
John McCarthy, M. Minsky· International Journal of Int...· 0 citations
This study investigates the integration of FA into edge computing ecosystems, leveraging advanced Privacy-Enhancing Technologies (PETs) such as Differential Privacy (DP), Secure Multiparty Computation (SMC), and Homomorphic Encryption (HE) to ensure robust privacy protections.
John McCarthy, M. Minsky· International Journal of Dat...· 0 citations
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