  Quantum control optimization algorithms are routinely used to generate
optimal quantum gates or efficient quantum state transfers. However, there are
two main challenges in designing efficient optimization algorithms, namely
overcoming the sensitivity to local optima and improving the computational
speed. The former challenge can be dealt with by designing hybrid algorithms,
such as a combination of gradient and simulated annealing methods. Here, we
propose and demonstrate the use of a machine learning method, specifically the
recommender system (RS), to deal with the latter challenge of enhancing
computational efficiency. We first describe ways to set up a rating matrix
involving gradients or gate fidelities. We then establish that RS can rapidly
and accurately predict elements of a sparse rating matrix. Using this approach,
we expedite a gradient ascent based quantum control optimization, namely GRAPE
and demonstrate the faster performance for up to 8 qubits. Finally, we describe
and implement the enhancement of the computational speed of a hybrid algorithm,
namely SAGRAPE.
