Robert Foskin

Reinforcement learning has had proven success in the domain of classical control and there has been a recent surge of work investigating its application in quantum control problems however not much work has focused on utilizing continuous measurement when training the agent. This is because the act of measurement on quantum systems is fundamentally different to that of the classical counterparts and introduces a number of unique challenges. The goal of this project will be to investigate the application of reinforcement learning to the control of quantum dynamics using continuous measurement-based feedback techniques. A key objective of this project will be the development of novel techniques for quantum state representation in reinforcement learning algorithms and further research in this area has the potential to impact the development of near term quantum technology, including a fault-tolerant quantum processor. In the area of feedback-based control, current methods have not shown sufficient progress. This is because such applications to quantum systems quickly become intractable for standard optimal control techniques when quantum feedback leads to an exponential increase in the search space. Analytical approaches are also difficult to realise consistently for quantum systems in experimental settings. Due to the presence of noise and de-coherence, optimal dynamics in an experimental system diverges from that of the model used when optimizing the control strategy. This is especially true in quantum feedback control where the act of observing the system continuously introduces non-linearity within the dynamics and generates measurement induced noisy dynamics. Established optimal control techniques have worked well for linear, unitary and deterministic systems, however no known generalized method exists for non-linear and stochastic systems. Reinforcement learning can be implemented in these settings because it is agnostic to the underlying physical description generating the observed dynamics. Control schemes can be derived heuristically using agent-based learning in a quantum environment. Such an approach would be adaptable and robust to changes in the environment and could be implemented more readily in experimental settings than model-based optimal control techniques. Beyond this, reinforcement learning has the potential to become a powerful simulation tool for quantum systems which cannot be analysed effectively by established methods such as systems with large Hilbert spaces, non-integrable systems and systems undergoing far from equilibrium dynamics. Learning-based approaches show promise as a way to probe the control landscapes of these quantum systems to