Speaker
Lukas J. Fiderer
(University Innsbruck)
Description
Next-generation quantum sensors are expected to outperform classical sensors. Since the success of these quantum sensors depends on the efficient use of limited resources (such as probe states and coherence time), we introduce the paradigm of smart quantum sensors, i.e., quantum sensors which make autonomous adjustments in order to optimize the measurement precision and to save resources. A suitable framework for smart quantum sensors is provided by the adaptive Bayesian approach to parameter estimation. We train neural networks to become fast and strong experiment-design heuristics that are shown to outperform established heuristics for the technologically important example of frequency estimation of a qubit that suffers from dephasing.
Authors
Lukas J. Fiderer
(University Innsbruck)
Jonas Schuff
(University Oxford)
Daniel Braun
(University Tübingen)
