Quantum Control

Quantum control aims to manipulate quantum systems with high precision, often to achieve specific computational or physical tasks. Current research heavily emphasizes the development and application of machine learning algorithms, particularly reinforcement learning and variational quantum circuits, for designing efficient and robust control strategies, including those based on pulse-based neural networks and quantum long short-term memory (QLSTM) models. These advancements are crucial for improving the performance and scalability of quantum technologies, impacting areas such as quantum computing, sensing, and materials science by enabling more precise control over complex quantum systems. Furthermore, research is actively exploring hardware acceleration techniques to make quantum control more efficient and practical.