论文标题
朝着启用AI的控制以增强量子转移
Towards AI-enabled Control for Enhancing Quantum Transduction
论文作者
论文摘要
随着量子互联网的出现，找到新的方法来连接分布式量子测试床并开发新颖的技术和研究，从而扩展了管理量子性能的创新，这变得至关重要。许多新兴技术都集中在量子中继器和专门的硬件上，以在特殊用途通道上扩展量子距离。但是，几乎没有利用当前的网络技术，投资于光学技术，与量子技术合并。在本文中，我们主张一个启用AI的控制，该控制允许在量子和光子能量之间进行优化，有效的转换，以使光学和量子设备一起工作。我们的方法将AI技术（例如深钢筋学习算法）与物理量子传感器相结合，以告知两个波长之间的实时转换。从模拟环境中学习，受过训练的AI启用传感器将导致最佳的量子转导，以最大化量子寿命。
With advent of quantum internet, it becomes crucial to find novel ways to connect distributed quantum testbeds and develop novel technologies and research that extend innovations in managing the qubit performance. Numerous emerging technologies are focused on quantum repeaters and specialized hardware to extend the quantum distance over special-purpose channels. However, there is little work that utilizes current network technology, invested in optic technologies, to merge with quantum technologies. In this paper we argue for an AI-enabled control that allows optimized and efficient conversion between qubit and photon energies, to enable optic and quantum devices to work together. Our approach integrates AI techniques, such as deep reinforcement learning algorithms, with physical quantum transducer to inform real-time conversion between the two wavelengths. Learning from simulated environment, the trained AI-enabled transducer will lead to optimal quantum transduction to maximize the qubit lifetime.
