Anales/Anales AFA

AUTOMATIC RECALIBRATION OF QUANTUM DEVICES BY REINFORCING LEARNING

T. Crosta, Lorena Rebón, Fernando Vilariño, J. M. Matera, M. Bilkis

Published Dec 29, 2025• 0 citations

Abstract

During their operation, due to shifts in environmental conditions, devices undergo various forms of detuning from theiroptimal 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 relieson precisely tuning their parameters. At the same time, the detailed modeling of the environmental behavior is oftencomputationally unaffordable, while a direct measure of the parameters defining the system state is costly and introducesextra noise in the mechanism. In this study, we investigate the application of reinforcement learning techniques todevelop a model-free control loop for continuous recalibration of quantum device parameters. Furthermore, we explorethe advantages of incorporating minimal environmental noise models. As an example, the application to numericalsimulations of a Kennedy receiver-based long-distance quantum communication protocol is presented.

DOI: 10.31527/analesafa.2025.36.4.95-105

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