通过强化学习控制量子多体系统中的非遍历性
Controlling nonergodicity in quantum many-body systems by reinforcement learning
AI总结：
该研究提出一种无模型深度强化学习框架，用于量子多体系统的非遍历性控制，在一维倾斜费米-哈伯德系统中验证了其有效性，相比传统方法控制场景更广，且具备实验可行性。
摘要：
寻找能抑制任意初始态量子热化的最优控制策略，即所谓的量子非遍历性控制，对量子信息科学与技术具有重要意义。以往的控制方法很大程度上依赖于目标量子系统的理论模型，但可逆的模型近似与不准确性可能导致控制失败。我们开发了一个用于量子非遍历性控制的无模型深度强化学习（DRL）框架。这是一种机器学习方法，其独特的核心是平衡探索与利用策略，以最大化累积奖励，从而在很长一段时间内，在含时非遍历度量中保留初始记忆。我们采用典型的一维倾斜费米-哈伯德（Fermi-Hubbard）系统进行验证，结果表明DRL智能体仅通过与环境的相互作用，就能高效地学习量子多体系统的特性。与Wannier-Stark局域化的特定协议等方法相比，DRL得到的最优策略为相图中的非遍历性调控提供了更广泛的控制场景。其连续控制协议与观测结果在实验上具备可行性。DRL的无模型特性及其在控制函数方面的灵活搜索空间，有望在更复杂的量子多体系统中实现非遍历性控制。
英文摘要：
Finding optimal control strategies to suppress quantum thermalization for arbitrarily initial states, the so-called quantum nonergodicity control, is important for quantum information science and technologies. Previous control methods largely relied on theoretical model of the target quantum system, but invertible model approximations and inaccuracies can lead to control failures. We develop a model-free and deep-reinforcement learning (DRL) framework for quantum nonergodicity control. It is a machine-learning method with the unique focus on balancing exploration and exploitation strategies to maximize the cumulative rewards so as to preserve the initial memory in the time-dependent nonergodic metrics over a long stretch of time. We use the paradigmatic one-dimensional tilted Fermi-Hubbard system to demonstrate that the DRL agent can efficiently learn the quantum many-body system solely through the interactions with the environment. The optimal policy obtained by the DRL provides broader control scenarios for managing nonergodicity in the phase diagram as compared to, e.g., the specific protocol for Wannier-Stark localization. The continuous control protocols and observations are experimentally feasible. The model-free nature of DRL and its versatile search space for control functions render promising nonergodicity control in more complex quantum many-body systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
