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Reinforcement Learning in Different Phases of Quantum Control

机译：不同阶段的增强学习量子控制

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The ability to prepare a physical system in a desired quantum state is central to many areas of physics such as nuclear magnetic resonance, cold atoms, and quantum computing. Yet, preparing states quickly and with high fidelity remains a formidable challenge. In this work, we implement cutting-edge reinforcement learning (RL) techniques and show that their performance is comparable to optimal control methods in the task of finding short, high-fidelity driving protocol from an initial to a target state in nonintegrable many-body quantum systems of interacting qubits. RL methods learn about the underlying physical system solely through a single scalar reward (the fidelity of the resulting state) calculated from numerical simulations of the physical system. We further show that quantum-state manipulation viewed as an optimization problem exhibits a spin-glass-like phase transition in the space of protocols as a function of the protocol duration. Our RL-aided approach helps identify variational protocols with nearly optimal fidelity, even in the glassy phase, where optimal state manipulation is exponentially hard. This study highlights the potential usefulness of RL for applications in out-of-equilibrium quantum physics.
机译：在所需量子状态下制备物理系统的能力是核磁共振，冷原子和量子计算的许多物理区域的核心。然而，迅速准备各国，高保真仍然是一个强大的挑战。在这项工作中，我们实施了尖端增强学习（RL）技术，并表明它们的性能与在非恒定许多身体中从初始到目标状态找到短，高保真驾驶协议的任务的最佳控制方法相互作用Qubits的量子系统。 RL方法仅通过单个标量奖励（由物理系统的数值模拟计算的单标量奖励（所产生状态的保真度）来了解底层物理系统。我们进一步示出了作为优化问题观看的量子状态操作在协议持续时间的函数中表现出协议空间中的旋转玻璃状相变。我们的RL-辅助方法有助于识别变分协议，即使在玻璃阶段也有几乎最佳的保真度，其中最佳状态操纵是指数艰难的。本研究突出了RL在均衡量超出量子物理学中的应用的潜在有用性。

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