  We devise powerful algorithms based on differential evolution for adaptive
many-particle quantum metrology. Our new approach delivers adaptive quantum
metrology policies for feedback control that are orders-of-magnitude more
efficient and surpass the few-dozen-particle limitation arising in methods
based on particle-swarm optimization. We apply our method to the
binary-decision-tree model for quantum-enhanced phase estimation as well as to
a new problem: a decision tree for adaptive estimation of the unknown bias of a
quantum coin in a quantum walk and show how this latter case can be realized
experimentally.
