  State-of-the-art quantum algorithms routinely tune dynamically parametrized
cost functionals for combinatorics, machine learning, equation-solving, or
energy minimization. However, large search complexity often demands many
(noisy) quantum measurements, leading to the increasing use of classical
probability models to estimate which areas in the cost functional landscape are
of highest interest. Introducing deep learning based modelling of the
landscape, we demonstrate an order of magnitude increases in accuracy and speed
over state-of-the-art Bayesian methods. Moreover, once trained the deep neural
network enables the extraction of information at a much faster rate than
conventional numerical simulation. This allows for on-the-fly experimental
optimizations and detailed classification of complexity and navigability
throughout the phase diagram of the landscape.
