Reinforcement Learning for the Precise Control of Quantum Systems
Quantum computing, characterised by the principles of quantum mechanics, demand sophisticated control strategies to achieve precise manipulation and optimization of quantum systems. Classical control methods may rely on pre-defined control pulses with limited scalability and adaptability for large quantum systems, or optimization algorithms which are time-consuming, computationally demanding and limited in their exploration capabilities that can lead to suboptimal solutions. This research seeks to explore the use of Reinforcement Learning (RL) methodologies to address the inherent challenges of dynamic and complex quantum system control. The aim is to contribute to the development of robust and precise quantum control policies/strategies that can adapt to uncertainties, cover vast solution spaces, respond in real-time to changes in quantum environments, system parameters, and unexpected quantum events.