Criticality-enhanced global frequency sensing with a monitored Kerr parametric oscillator via extended Kalman filter
AI Breakdown
Get a structured breakdown of this paper — what it's about, the core idea, and key takeaways for the field.
Abstract
We analyze a global sensing scenario in which the frequency of a monitored Kerr parametric oscillator is estimated assuming limited prior information. The frequency is estimated in real-time by continuously monitoring the oscillator quadrature through homodyne detection and processing the resulting photocurrent with an extended Kalman filter (EKF). Due to the sensor nonlinearity, individual EKF trajectories do not always converge to the true unknown frequency in the long-time limit. However, we show that the statistical distribution of the frequency estimates does exhibit a sharp peak around the true value in the same limit. Leveraging this key statistical property, we develop a global sensing protocol assisted by adaptive control of the sensor parameters to harness critical enhancement. We present numerical evidence that this criticality-enhanced frequency estimation remains robust under low detection efficiency.