AUTOMATIC RECALIBRATION OF QUANTUM DEVICES BY REINFORCING LEARNING

Abstract

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.