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arXiv 2406.04550quant-phphysics.app-phphysics.optics
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光机械系统的纠缠工程与强化学习

Entanglement engineering of optomechanical systems by reinforcement learning

Li-Li Ye, Christian Arenz, Joseph M. Lukens, Ying-Cheng Lai

AI总结:

研究针对量子系统纠缠工程中纠缠脆弱性与实验表征的难题,提出一种无模型深度强化学习方法,结合反馈控制与弱连续测量来生成并维持量子纠缠,并在量子光机械系统中验证了其有效性,为实验量子系统提供了通用控制原理。

摘要:

纠缠是量子信息科学和技术的基础,然而对于任意量子系统,控制和操纵纠缠——即所谓的纠缠工程——仍然是一个艰巨的挑战。存在两个困难:量子纠缠的脆弱性及其实验表征。我们开发了一种无模型深度强化学习(RL)方法用于纠缠工程,其中利用反馈控制以及弱连续测量和部分状态观测来生成并维持所需的纠缠。我们采用具有线性或非线性光子-声子相互作用的量子光机械系统来演示我们基于机器学习的纠缠工程协议的工作原理。具体而言,RL智能体与一个或多个并行的量子光机械环境依次交互,收集轨迹,并更新策略以最大化累积奖励,从而在任意时间内创建并稳定量子纠缠。这种基于机器学习的无模型控制原理普遍适用于一般实验量子系统的纠缠工程。

英文摘要:

Entanglement is fundamental to quantum information science and technology, yet controlling and manipulating entanglement -- so-called entanglement engineering -- for arbitrary quantum systems remains a formidable challenge. There are two difficulties: the fragility of quantum entanglement and its experimental characterization. We develop a model-free deep reinforcement-learning (RL) approach to entanglement engineering, in which feedback control together with weak continuous measurement and partial state observation is exploited to generate and maintain desired entanglement. We employ quantum optomechanical systems with linear or nonlinear photon-phonon interactions to demonstrate the workings of our machine-learning-based entanglement engineering protocol. In particular, the RL agent sequentially interacts with one or multiple parallel quantum optomechanical environments, collects trajectories, and updates the policy to maximize the accumulated reward to create and stabilize quantum entanglement over an arbitrary amount of time. The machine-learning-based model-free control principle is applicable to the entanglement engineering of experimental quantum systems in general.

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。

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