Spin estimation plays a crucial role in quantum metrology, particularly in systems like atomic magnetometers where atomic spin ensembles transduce magnetic-field information into measurable optical signals. However, precise spin estimation remains challenging, especially in weak polarization schemes where signals are often obscured by both classical and quantum noise. Conventional fitting methods for spin signal and variance estimation are computationally intensive, lack real-time capabilities, and exhibit sensitivity to stochastic fluctuations and data-acquisition parameters. To address these challenges, we propose and demonstrate the implementation of a Kalman filter (KF) combined with a Rauch-Tung-Striebel (RTS) smoother for enhanced spin estimation across various polarization conditions. Our comprehensive analysis of spin conditional variance estimation reveals that the KF method achieves a remarkable 4.92-dB noise suppression compared to conventional fitting methods, while the RTS smoother provides further enhancement with 6.94-dB suppression that enables quantum noise squeezing observation. These findings offer new tools for quantum noise analysis and signal recovery, offering significant potential for advancing the performance of quantum-enhanced sensors and other precision measurement technologies.
Spin estimation plays a crucial role in quantum metrology, particularly in systems like atomic magnetometers where atomic spin ensembles transduce magnetic-field information into measurable optical signals. However, precise spin estimation remains challenging, especially in weak polarization schemes where signals are often obscured by both classical and quantum noise. Conventional fitting methods for spin signal and variance estimation are computationally intensive, lack real-time capabilities, and exhibit sensitivity to stochastic fluctuations and data-acquisition parameters. To address these challenges, we propose and demonstrate the implementation of a Kalman filter (KF) combined with a Rauch-Tung-Striebel (RTS) smoother for enhanced spin estimation across various polarization conditions. Our comprehensive analysis of spin conditional variance estimation reveals that the KF method achieves a remarkable 4.92-dB noise suppression compared to conventional fitting methods, while the RTS smoother provides further enhancement with 6.94-dB suppression that enables quantum noise squeezing observation. These findings offer new tools for quantum noise analysis and signal recovery, offering significant potential for advancing the performance of quantum-enhanced sensors and other precision measurement technologies.
Sensing a magnetic field with an atomic magnetometer operated in real time presents significant challenges, primarily due to sensor nonlinearity, the presence of noise, and the need for one-shot estimation. To address these challenges, we propose a comprehensive approach that integrates measurement, estimation, and control strategies. Specifically, this involves implementing a quantum nondemolition measurement based on continuous light probing of the atomic ensemble. The resulting photocurrent is then directed into an extended Kalman filter to produce instantaneous estimates of the system’s dynamical parameters. These estimates, in turn, are utilized by a linear quadratic regulator, whose output is applied back to the system through a feedback loop. This procedure automatically steers the atomic ensemble into a spin-squeezed state, yielding a quantum enhancement in precision. Furthermore, thanks to the feedback proposed, the atoms exhibit entanglement even when the measurement data are discarded. To prove that our approach constitutes the optimal strategy in realistic scenarios, we derive ultimate bounds on the estimation error applicable in the presence of both local and collective decoherence, and show that these are indeed attained. Additionally, we demonstrate that, for large ensembles, the extended Kalman filter not only reliably predicts its own estimation error in real time, but also accurately estimates spin squeezing at short timescales.
The extraction of weak signals plays a crucial role in quantum precision measurement, where the estimation results are often limited by low signal-to-noise ratios. Here, we demonstrate a parameter-estimation framework based on the adaptive extended Kalman filter for dynamic magnetic-field estimation in quantum systems using spin-noise measurements -- a challenging regime characterized by weak signals. By modeling the magnetic field as an unknown parameter, the proposed approach alleviates model dependence in state estimation. Furthermore, by introducing an adaptive algorithm with real-time noise estimation, our method overcomes the measurement noise intensity constraints of conventional extended Kalman filtering and enhances its practical applicability. Numerical simulations covering three representative magnetic-field dynamics validate the capability of the proposed framework, while experimental results demonstrate successful tracking of a seismo-magnetic-like signal beyond the intrinsic sensitivity of conventional spin-noise spectroscopy.
The shot-noise detection limit in current high-precision magnetometry [Nature (London) 422, 596 (2003)] is a manifestation of quantum fluctuations that scale as 1/sqrt[N] in an ensemble of N atoms. Here, we develop a procedure that combines continuous measurement and quantum Kalman filtering [Rep. Math. Phys. 43, 405 (1999)]] to surpass this conventional limit by exploiting conditional spin squeezing to achieve 1/N field sensitivity. Our analysis demonstrates the importance of optimal estimation for high bandwidth precision magnetometry at the Heisenberg limit and also identifies an approximate estimator based on linear regression.
Quantum entanglement, in the form of spin squeezing, is known to improve the sensitivity of atomic sensors to static or slowly varying fields. Sensing transient events presents a distinct challenge, requires different analysis tools, and has not been shown to benefit from entanglement in practically important scenarios such as spin-precession magnetometry. To address this, we apply concepts from continuous quantum measurements and estimation theory to optical atomic magnetometers, aiming to accurately model these devices, interpret their measurement data, control their dynamics, and achieve optimal sensitivity. Quantifying this optimal performance requires determining a fundamental quantum limit on sensitivity. We derive this limit, imposed by noise, and show that it scales at best linearly with sensing time and atom number N, ruling out any super-classical scaling. This limit is independent of the initial state, measurement, estimator, and measurement-based feedback, and depends only on the decoherence model and the strength of field fluctuations. Thus, finding an estimator that attains this bound proves the sensing strategy optimal. To approach this limit, we develop a quantum dynamical model scalable with N, based on a co-moving Gaussian approximation of the stochastic master equation, which includes measurement backaction and decoherence. This enables a real-time estimation and control architecture integrating an extended Kalman filter with a linear quadratic regulator. Simulating the magnetometer with our model and EKF+LQR strategy shows that quantum-limited tracking of constant and fluctuating fields is within reach of current atomic magnetometers. Our sensing strategy can also track biologically relevant signals, such as heartbeat-like waveforms, and drive the atomic ensemble into an entangled state, even when the measurement record is used for feedback but later discarded.
Abstract Continuously monitored atomic spin-ensembles allow, in principle, for real-time sensing of external magnetic fields beyond classical limits. Within the linear-Gaussian regime, thanks to the phenomenon of measurement-induced spin-squeezing, they attain a quantum-enhanced scaling of sensitivity both as a function of time, t, and the number of atoms involved, N. In our work, we rigorously study how such conclusions based on Kalman filtering methods change when inevitable imperfections are taken into account: in the form of collective noise, as well as stochastic fluctuations of the field in time. We prove that even an infinitesimal amount of noise disallows the error to be arbitrarily diminished by simply increasing N, and forces it to eventually follow a classical-like behaviour in t. However, we also demonstrate that, ‘thanks’ to the presence of noise, in most regimes the model based on a homodyne-like continuous measurement actually achieves the ultimate sensitivity allowed by the decoherence, yielding then the optimal quantum-enhancement. We are able to do so by constructing a noise-induced lower bound on the error that stems from a general method of classically simulating a noisy quantum evolution, during which the stochastic parameter to be estimated—here, the magnetic field—is encoded. The method naturally extends to schemes beyond the linear-Gaussian regime, in particular, also to ones involving feedback or active control.
Integrating classical estimation and control theory with quantum systems holds potential for quantum measurement improvement. Here, we report a real-time linear quadratic regulator (LQR) optimal control for spin damping in electron-nuclear spin-coupling regime. The aim is a quicker response and enhanced noise suppression for precision quantum measurements. Using a Kalman quantum observer with electron spin data, we estimate the quantum state of nondirectly observable nuclear spin. LQR full-state feedback optimal control is applied to achieve faster spin-coupling damping. Demonstrated in an atomic spin gyroscope, our method significantly enhances dynamic response with a 4.3-fold improvement in rotational bandwidth. It also boosts long-term stability and measurement sensitivity by 62.9%, presenting a promising avenue to elevate spin-coupling ensemble performance in quantum measurements.