Automatic re-calibration of quantum devices by reinforcement learning
This addresses the challenge of maintaining optimal settings in quantum devices, which is critical for reliable quantum communication, though it appears incremental as it builds on existing reinforcement learning techniques.
The study tackled the problem of quantum device detuning from environmental shifts by developing a reinforcement learning-based model-free control loop for continuous recalibration, applied to a Kennedy receiver-based quantum communication protocol in simulations.
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.