Reinforcement learning based methods for optimal control and design of quantum systems

We apply such methodology to Quantum Thermodynamics, reducing the dissipation and irreversibility arising from non-quasistatic transformations of closed quantum systems, and in the context of coherent population transfer, realizing almost perfect transfer between the lower energy states of a three-level lambda system. In the latter case we also combine the proposed Reinforcement Learning based approach with numerical optimization techniques to further improve optimal solutions.<br><br>We then shift our attention to quantum system design and use Reinforcement Learning to find optimal qubit chain configurations for end-to-end excitation-transfer.

In general, Reinforcement Learning can be deployed for optimal quantum systems design when the problem of designing a physical system for a given task can be translated into a problem of optimal decision making. This is especially useful when we have limited knowledge on the system properties, we need to optimize discrete variables or perform multiple optimizations under different conditions.