2024/04/16 by T. Crosta, Lorena Rebón, Crosta, T. +7
Computer Science · #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph)
paper · pdf · doi:10.48550/arxiv.2404.10726
openalex publication_date 2024/04/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.