Sequential analysis in a continuous spin-noise quantum sensor
Elisabet Roda-Salichs, Giulio Gasbarri, Antoni Alou, Michalis Skotiniotis, Aleksandra Sierant, Diana Méndez-Avalos, Morgan W. Mitchell, John Calsamiglia
Many control and detection applications require real-time analysis of signals from sensors, in order to quickly and accurately act upon events revealed by the sensors. Such signal analysis benefits from statistical models of signal and sensor behavior. This creates a need for data analysis methods that are simultaneously model based, computationally efficient, and causal, in the sense that they employ only sensor data available prior to a specific point in time. In this work, we implement sequential data analysis techniques on a spin-noise-based quantum sensor to perform two key tasks: hypothesis testing and quickest change-point detection. These online protocols allow us to detect weak magnetic fields by adaptively collecting measurement data until a predefined confidence threshold is reached. We demonstrate these methods in a realistic experimental setting and derive performance bounds for the achievable precision and response time. Our approach has potential utility when detecting small perturbations to the magnetic field, in both applied and fundamental contexts including biomagnetism, geophysical surveys, detection of concealed materials, searches for dark matter candidates, and exotic spin interactions. Our results demonstrate that sequential techniques enable faster and more sensitive detection, making them a powerful tool for quantum sensing.
Many control and detection applications require real-time analysis of signals from sensors, in order to quickly and accurately act upon events revealed by the sensors. Such signal analysis benefits from statistical models of signal and sensor behavior. This creates a need for data analysis methods that are simultaneously model based, computationally efficient, and causal, in the sense that they employ only sensor data available prior to a specific point in time. In this work, we implement sequential data analysis techniques on a spin-noise-based quantum sensor to perform two key tasks: hypothesis testing and quickest change-point detection. These online protocols allow us to detect weak magnetic fields by adaptively collecting measurement data until a predefined confidence threshold is reached. We demonstrate these methods in a realistic experimental setting and derive performance bounds for the achievable precision and response time. Our approach has potential utility when detecting small perturbations to the magnetic field, in both applied and fundamental contexts including biomagnetism, geophysical surveys, detection of concealed materials, searches for dark matter candidates, and exotic spin interactions. Our results demonstrate that sequential techniques enable faster and more sensitive detection, making them a powerful tool for quantum sensing.
Detecting abrupt changes in data streams is crucial because they are often triggered by events that have important consequences if left unattended. Quickest change-point detection has become a vital sequential analysis primitive that aims at designing procedures that minimize the expected detection delay of a change subject to a bounded expected false alarm time. We put forward the quantum counterpart of this fundamental primitive on streams of quantum data. We give a lower bound on the mean minimum delay when the expected time of a false alarm is asymptotically large, under the most general quantum detection strategy, which is given by a sequence of adaptive collective (potentially weak) measurements on the growing string of quantum data. In addition, we give particular strategies based on repeated measurements on independent blocks of samples that asymptotically attain the lower bound and thereby establish the ultimate quantum limit for quickest change-point detection. Finally, we discuss online change-point detection in quantum channels.
Quantum sensors offer significant advantages over classical devices in spatial resolution and sensitivity, enabling transformative applications across materials science, healthcare, and beyond. Their practical performance, however, is often constrained by unmodelled effects, including noise, imperfect state preparation, and non-ideal control fields. In this work, we report the first experimental implementation of a graybox modelling strategy for a solid-state open quantum system. The graybox framework integrates a physics-based system model with a data-driven description of experimental imperfections, achieving higher fidelity than purely analytical (whitebox) approaches while requiring fewer training resources than fully deep-learning blackbox models. We experimentally validate the method on the task of estimating a static magnetic field using a single-spin quantum sensor, performing Bayesian inference with a graybox model trained on prior experimental data. Using roughly 10,000 training datapoints, the graybox model yields several orders of magnitude improvement in mean squared error over the corresponding physics-only model, as well as significant improvement over a blackbox model with comparable size. These results are broadly applicable to a wide range of quantum sensing platforms, not limited to single-spin systems, and are particularly valuable for real-time adaptive protocols, where model inaccuracies can otherwise lead to suboptimal control and degraded performance.
The excellent sensitivities of quantum sensors are a double-edged sword: minuscule quantities can be observed, but any undesired signal acts as noise. This is challenging when detecting quantities that are obscured by such noise. Decoupling sequences improve coherence times and hence sensitivities, though only AC signals in narrow frequency bands are distinguishable. Alternatively, comagnetometers operate gaseous spin mixtures at high temperatures in the self-compensating regime to counteract slowly varying noise. These are applied with great success in various exotic spin-interaction searches. Here, we propose a method that decouples specific DC fields from DC and AC magnetic noise. It requires any spin cluster where the effect on each individual spin is different for the target field and local magnetic fields, which allows for a different approach compared to comagnetometers. The presented method has several key advantages, including an orders-of-magnitude increase in noise frequencies to which we are resistant. We explore electron-spin nuclear-spin pairs in nitrogen-vacancy centres in diamond, with a focus on their merit for light dark-matter searches. Other applications include gradient sensing, quantum memory, and gyroscopes.
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 Quantum systems are promising candidates for sensing of weak signals as they can be highly sensitive to external perturbations, thus providing excellent performance when estimating parameters of external fields. However, when trying to detect weak signals that are hidden by background noise, the signal-to-noise ratio is a more relevant metric than raw sensitivity. We identify, under modest assumptions about the statistical properties of the signal and noise, the optimal quantum control to detect an external signal in the presence of background noise using a quantum sensor. Interestingly, for white background noise, the optimal solution is the simple and well-known spin-locking control scheme. Using numerical techniques, we further generalize these results to the case of background noise with a Lorentzian spectrum. We show that for increasing correlation time, pulse based sequences, such as CPMG, are also close to the optimal control for detecting the signal, with the crossover dependent on the signal frequency. These results show that an optimal detection scheme can be easily implemented in near-term quantum sensors without the need for complicated pulse shaping.
Single-spin quantum sensors, for example based on nitrogen-vacancy centres in diamond, provide nanoscale mapping of magnetic fields. In applications where the magnetic field may be changing rapidly, total sensing time is crucial and must be minimised. Bayesian estimation and adaptive experiment optimisation can speed up the sensing process by reducing the number of measurements required. These protocols consist of computing and updating the probability distribution of the magnetic field based on measurement outcomes and of determining optimized acquisition settings for the next measurement. However, the computational steps feeding into the measurement settings of the next iteration must be performed quickly enough to allow real-time updates. This article addresses the issue of computational speed by implementing an approximate Bayesian estimation technique, where probability distributions are approximated by a finite sum of Gaussian functions. Given that only three parameters are required to fully describe a Gaussian density, we find that in many cases, the magnetic field probability distribution can be described by fewer than ten parameters, achieving a reduction in computation time by factor 10 compared to existing approaches. ForT2*=1μs, only a small decrease in computation time is achieved. However, in these regimes, the proposed Gaussian protocol outperforms the existing one in tracking accuracy.