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 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.
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.
In this paper we propose to estimate the state of a quantum system dynamically evolving in the presence of white Gaussian noise (WGN) under Schrödinger dynamics using Extended Kalman Filter (EKF) algorithm. In the proposed measurement model the EKF is applied to obtain the conditional expectation of the state at a given observation time up to that time. First the complex Schrödinger equation of dimension N is transformed into a real Schrödinger equation of dimension 2N. MATLAB programs were implemented to plot the wave function, error and noise to signal ratio (NSR). NSR within minimum bounds is obtained.
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.
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.
The accuracy of quantum state estimation and dynamic signal tracking is crucial for the reliability and performance of quantum sensing instruments such as spin-exchange relaxation-free (SERF) co-magnetometers and gyroscopes. To improve the performance of the SERF gyroscope for future quantum navigation applications, an adaptive quantum estimation and closed-loop optimal control method based on fault detection and isolation (FDI) and an adaptive Kalman filter is proposed. Firstly, the system model of the SERF gyroscope is established, and a real-time angular velocity measurement method is provided. Then, the stochastic model of the SERF gyroscope is analyzed based on an adaptive quantum Kalman observer to track and compensate for real-time angular velocity error with the assistance of FDI. Finally, the real-time LQI (Linear-Quadratic-Integral) control method is adopted to improve the dynamic response and accuracy of the overall system. Simulations and experiments are conducted based on the SERF gyroscope platform. The results show that the system can achieve optimal tracking accuracy for polarization estimation and effectively isolate fault information. The dynamic response time has been improved by 58.2% under environmental disturbance, which provides a foundation for subsequent navigation applications.
Localized spins in the solid state are attracting widespread attention as highly sensitive quantum sensors with nanoscale spatial resolution and fascinating applications. Recently, adaptive measurements were used to improve the dynamic range for spin-based sensing of deterministic Hamiltonian parameters. Here we explore a very different direction—spin-based adaptive sensing of random noises. First, we identify distinguishing features for the sensing of magnetic noises compared with the estimation of deterministic magnetic fields, such as the different dependences on the spin decoherence, the different optimal measurement schemes, the absence of the modulo-2 π phase ambiguity, and the crucial role of adaptive measurement. Second, we perform numerical simulations that demonstrate significant speed up of the characterization of the spin decoherence time via adaptive measurements. This paves the way towards adaptive noise sensing and coherence protection.