During their operation, due to shifts in environmental conditions, devices
undergo various forms of detuning from their optimal settings. Typically, this
is addressed through control loops, which monitor variables and the device
performance, to maintain settings at their optimal values. Quantum devices are
particularly challenging since their functionality relies on precisely tuning
their parameters. At the same time, the detailed modeling of the environmental
behavior is often computationally unaffordable, while a direct measure of the
parameters defining the system state is costly and introduces extra noise in
the mechanism. In this study, we investigate the application of reinforcement
learning techniques to develop a model-free control loop for continuous
recalibration of quantum device parameters. Furthermore, we explore the
advantages of incorporating minimal environmental noise models. As an example,
the application to numerical simulations of a Kennedy receiver-based
long-distance quantum communication protocol is presented.

通过使用强化学习技术和环境噪声模型，我们研究了量子设备参数的连续校准的无模型控制循环，并以肯尼迪接收器为基础的远距离量子通信协议的数值模拟应用作为示例。

通过强化学习自动重新校准量子设备

Automatic re-calibration of quantum devices by reinforcement learning

Progress in several areas of computer science has been enabled by comfortable
and efficient means of experimentation, clear interfaces, and interchangable
components, for example using OpenCV for computer vision or ROS for robotics.
We describe an extension of the Acumos system towards enabling the above
features for general AI applications. Originally, Acumos was created for
telecommunication purposes, mainly for creating linear pipelines of machine
learning components. Our extensions include support for more generic components
with gRPC/Protobuf interfaces, automatic orchestration of graphically assembled
solutions including control loops, sub-component topologies, and event-based
communication,and provisions for assembling solutions which contain user
interfaces and shared storage areas. We provide examples of deployable
solutions and their interfaces. The framework is deployed at
this http URL and its source code is managed as an open source
Eclipse project.

通过扩展 Acumos 系统来支持 AI 应用程序的通用组件、自动编排和用户界面，该系统部署在开源 Eclipse 项目中并提供示例。