  Dynamical decoupling (DD) is a low-overhead method for quantum error
suppression. We describe how empirical learning schemes can be used to tailor
DD strategies to the quantum device and task at hand. We use genetic algorithms
to learn DD (GADD) strategies and apply our method to the 27-qubit
Bernstein-Vazirani algorithm, 5-qubit Grover's algorithm, and 80-qubit mirror
randomized benchmarking circuits. In each scenario, the GADD strategies
significantly outperform canonical DD sequences. We demonstrate the generic and
scalable nature of our GADD method in that it does not require a priori
knowledge of target circuit outcomes and has runtime remaining constant with
increasing circuit depth and qubit number. Moreover, the relative improvement
of empirically learned DD strategies over canonical DD sequences is shown to
increase with increasing problem size and circuit sophistication.
