量子控制不同阶段的强化学习
Reinforcement Learning in Different Phases of Quantum Control
摘要：
在所需的量子态中制备物理系统的能力是物理学的许多领域的核心，例如核磁共振，冷原子和量子计算。然而，迅速和高度逼真地准备国家仍然是一个巨大的挑战。在这项工作中，我们实现了尖端的强化学习（RL）技术，并表明它们的性能与最优控制方法相当，可以在相互作用量子位的不可积多体量子系统中找到从初始状态到目标状态的短，高保真的驱动协议。RL方法仅通过从物理系统的数值模拟计算的单个标量奖励（结果状态的保真度）来了解底层物理系统。我们进一步表明，量子态操纵，被视为一个优化问题，表现出一个自旋玻璃状的相变在协议的空间作为协议持续时间的函数。我们的RL辅助的方法有助于确定变分协议与接近最佳的保真度，即使在玻璃相，最佳状态操纵是指数困难。这项研究突出了RL在非平衡量子物理学应用中的潜在有用性。
英文摘要：
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 non-integrable 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.
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