Key research themes
1. How do quantum neural networks generalize classical neural architectures to improve learning and information processing?
This theme explores the development of quantum generalizations of classical neural networks, focusing on their theoretical frameworks, training methodologies, and practical advantages. The research investigates how quantum neural networks can leverage superposition, entanglement, and unitary transformations to outperform classical counterparts in efficiency and capability, particularly for quantum data and protocols.
2. What frameworks and models enable the integration of quantum computation within cyber-physical and autonomous systems?
This research area focuses on developing theoretical and applied frameworks to incorporate quantum computing into cyber-physical systems (CPS), autonomous robotics, and decision-making architectures. It investigates multilayered quantum networks, quantum algorithm-driven control systems, and quantum-enhanced planning to improve system resilience, decision accuracy, and computational efficiency in complex physical and industrial environments.
3. How do foundational theoretical frameworks in quantum cybernetics and noocybernetics advance understanding of mind, consciousness, and intelligence in quantum domains?
This theme investigates formal theoretical models that combine quantum theory with cybernetics principles to represent and analyze mind evolution, consciousness, and intelligence. It includes establishing mathematical formalisms in infinite-dimensional Hilbert spaces, exploring nonlocal consciousness dependencies, and proposing quantum-inspired protocols for the evolution and optimization of mental states and AI systems beyond classical computational paradigms.