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 control protocols that can generate many different target states. 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.

量子信息处理通常需要准备任意的量子状态，例如二级系统的布洛赫球上的所有状态。虽然数值优化可以准备各个目标状态，但它们缺乏找到可以生成许多不同目标状态的通用控制协议的能力。在这里，我们通过准备具有深度强化学习的连续状态集来演示全局量子控制。使用神经网络表示协议，该网络将协议自动分为相似的类型，这对于查找协议类别和提取物理见解可能很有用。作为应用，我们在复杂的多级氮空位中心为电子自旋生成任意叠加态，从而揭示了以特定制备时标为特征的协议类别。