Coherent transport of quantum states by deep reinforcement learning

Some problems in physics can be handled only after a suitable ansatz solution has been guessed, proving to be resilient to generalization. The coherent transport of a quantum state by adiabatic passage through an array of semiconductor quantum dots is an excellent example of such a problem, where it is necessary to introduce a so-called counterintuitive control sequence. Instead, the deep reinforcement learning (DRL) technique has proven to be able to solve very complex sequential decision-making problems, despite a lack of prior knowledge. We show that DRL discovers a control sequence that outperforms the counterintuitive control sequence. DRL can even discover novel strategies when realistic disturbances affect an ideal system, such as detuning or when dephasing or losses are added to the master equation. DRL is effective in controlling the dynamics of quantum states and, more generally, whenever an ansatz solution is unknown or insufficient to effectively treat the problem.Many problems in physics do not have an exact solution method, so their resolution has been sometimes possible only by guessing test functions. The authors apply Deep Reinforcement Learning (DRL) to control coherent transport of quantum states in arrays of quantum dots and demonstrate that DRL can solve the control problem in the absence of a known analytical solution even under disturbance conditions.