Reinforcement Learning in Different Phases of Quantum Control
Reinforcement Learning in Different Phases of Quantum Control
复制标题
DOI：
10.1103/physrevx.8.031086
复制
发表时间：
2018-09-27
影响因子：
12.5
通讯作者：
Mehta, Pankaj
中科院分区：
文献类型：
--
作者：
Bukov, Marin;Day, Alexandre G. R.;Mehta, Pankaj
在所需量子态下准备物理系统的能力是核磁共振、冷原子和量子计算等许多物理学领域的核心。然而，快速、高度保真地让各国做好准备仍然是一项艰巨的挑战。在这项工作中，我们实现了尖端的强化学习（RL）技术，并表明，在相互作用量子位的不可积多体量子系统中寻找从初始状态到目标状态的短的、高保真驱动协议的任务中，它们的性能可与最优控制方法相媲美。强化学习方法仅通过物理系统数值模拟计算出的单个标量奖励（结果状态的保真度）来了解底层物理系统。我们进一步表明，被视为优化问题的量子态操纵在协议空间中表现出类似自旋玻璃的相变，作为协议持续时间的函数。我们的强化学习辅助方法有助于识别具有近乎最佳保真度的变分协议，即使在最佳状态操作难度呈指数级增长的玻璃相中也是如此。这项研究强调了强化学习在非平衡量子物理应用中的潜在用途。
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.