Środowiskowe Seminarium z Informacji i Technologii Kwantowych

We study causal waveform estimation (tracking) of time-varyingsignals in a paradigmatic atomic sensor, an alkali vapor monitored byFaraday rotation probing. We use Kalman filtering, which optimallytracks known linear Gaussian stochastic processes, to estimatestochastic input signals that we generate by optical pumping.Comparing the known input to the estimates, we confirm the accuracy ofthe atomic statistical model and the reliability of the Kalman filter,allowing recovery of waveform details far briefer than the sensor'sintrinsic time resolution. With proper filter choice, we obtainsimilar benefits when tracking partially-known and non-Gaussian signalprocesses, as are found in most practical sensing applications. Themethod evades the trade-off between sensitivity and time resolution incoherent sensing.