  Quantum control is valuable for various quantum technologies such as
high-fidelity gates for universal quantum computing, adaptive quantum-enhanced
metrology, and ultra-cold atom manipulation. Although supervised machine
learning and reinforcement learning are widely used for optimizing control
parameters in classical systems, quantum control for parameter optimization is
mainly pursued via gradient-based greedy algorithms. Although the quantum
fitness landscape is often compatible with greedy algorithms, sometimes greedy
algorithms yield poor results, especially for large-dimensional quantum
systems. We employ differential evolution algorithms to circumvent the
stagnation problem of non-convex optimization. We improve quantum control
fidelity for noisy system by averaging over the objective function. To reduce
computational cost, we introduce heuristics for early termination of runs and
for adaptive selection of search subspaces. Our implementation is massively
parallel and vectorized to reduce run time even further. We demonstrate our
methods with two examples, namely quantum phase estimation and quantum gate
design, for which we achieve superior fidelity and scalability than obtained
using greedy algorithms.
