We develop a deep learning (DL) framework assisted by differentiable programming for discovery of optimal quantum control protocols under hard constraints. To that end, we use neural network representations to our protocols, whose learning process is done with exact gradients. We find high quality solutions to the optimization problem of finite-time thermodynamical process in a quantum thermal machine. Using this DL algorithm, we show that a previously employed, intuitive energetic cost of the thermal machine driving suffers from a fundamental flaw, which we resolve with an alternative construction for the cost function.

I will describe the algorithm and will advocate how this DL-quantum control framework can be utilized to solve other quantum dynamics and thermodynamics problems.