  While a large number of algorithms for optimizing quantum dynamics for
different objectives have been developed, a common limitation is the reliance
on good initial guesses, being either random or based on heuristics and
intuitions. Here we implement a tabula rasa deep quantum exploration version of
the Deepmind AlphaZero algorithm for systematically averting this limitation.
AlphaZero employs a deep neural network in conjunction with deep lookahead in a
guided tree search, which allows for predictive hidden variable approximation
of the quantum parameter landscape. To emphasize transferability, we apply and
benchmark the algorithm on three classes of control problems using only a
single common set of algorithmic hyperparameters. AlphaZero achieves
substantial improvements in both the quality and quantity of good solution
clusters compared to earlier methods. It is able to spontaneously learn
unexpected hidden structure and global symmetry in the solutions, going beyond
even human heuristics.
