Automatic re-calibration of quantum devices by reinforcement learning

arXiv:2404.10726v1 Announce Type: cross
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 …

abstract arxiv control cs.lg devices environmental forms loops performance quant-ph quantum reinforcement reinforcement learning through type values variables