Quantum information processing often requires the preparation of arbitrary quantum states, such as all the states on the Bloch sphere for two-level systems. While numerical optimization can prepare individual target states, they lack the ability to find general solutions that work for a large class of states in more complicated quantum systems. Here, we demonstrate global quantum control by preparing a continuous set of states with deep reinforcement learning. The protocols are represented using neural networks, which automatically groups the protocols into similar types, which could be useful for finding classes of protocols and extracting physical insights. As application, we generate arbitrary superposition states for the electron spin in complex multi-level nitrogen-vacancy centers, revealing classes of protocols characterized by specific preparation timescales. Our method could help improve control of near-term quantum computers, quantum sensing devices and quantum simulations.
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