Closed System

Closed systems research focuses on understanding and controlling systems with defined boundaries, where internal interactions are paramount. Current efforts concentrate on developing model-free control methods, particularly using deep reinforcement learning and neural/tensor networks, to manage complex quantum systems and improve the precision of their simulations. This work is crucial for advancing quantum technologies and for developing a more robust systems engineering framework, particularly for designing and controlling intelligent systems, by addressing the challenges posed by their inherent feedback loops and interactions with their environment. The resulting advancements will have significant implications for fields ranging from quantum computing to artificial intelligence.