Coherent transport of quantum states by deep reinforcement learning

Some problems in physics are solved as a result of the discovery of an ansatz solution, namely a successful test guess, but unfortunately there is no general method to generate one. Recently, machine learning has increasingly proved to be a viable tool for modeling hidden features and effective rules in complex systems. Among the classes of machine learning algorithms, deep reinforcement learning (DRL)1 is providing some of the most spectacular results due to its ability to identify strategies for achieving a goal in a complex space of solutions without prior knowledge of the system2,3,4,5,6,7. Contrary to supervised learning, which has already been applied to quantum systems, such as in the determination of high-fidelity gates and the optimization of quantum memories by dynamic decoupling8, DRL has only very recently been proposed for the control of quantum systems9,10,11,12,13,14,15,16, along with a strictly quantum reinforcement learning implementation14,17. To show the power of DRL, we apply DRL to the problem of coherent transport by adiabatic passage (CTAP) where an electron (encoding the quantum state) is transferred through an array of quantum dots.