Automatic re-calibration of quantum devices by reinforcement learning
T. Crosta L. Rebón F. Vilariño J. M. Matera M. Bilkis
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.