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Quantum Science and Technology

Quantum magnetometry enhanced by machine learning

Isabell Jauch, Thomas Strohm, Tino Fuchs, Fedor Jelezko

Published Jan 20, 2026• 2 citations
Publisher

Abstract

Abstract Quantum optimal control in color center physics plays a crucial role in advancing sensor technology. This study focuses on optimizing microwave pulse shapes within a Ramsey sequence for nitrogen-vacancy centers to enhance sensor sensitivity and signal detection capabilities. We compare state-of-the-art optimization methods, including the dressed chopped randomized basis Nelder–Mead algorithm and covariance matrix adaptation evolutionary strategy, and extend our search to machine learning approaches, such as Gaussian processes and artificial neural networks. These machine learning techniques are specifically designed to provide robust and global solutions that can rapidly adapt to changing environmental conditions. Our results demonstrate more than a sixfold increase in convergence speed compared to conventional methods and considerable contrast improvements with a limited retraining set of 72 samples. Furthermore, we demonstrate that the optimized Ramsey contrast translates into a significant enhancement in the signal-to-noise ratio for detecting synthetic magnetic heart signals. This highlights the potential of machine learning-driven quantum optimal control for developing more flexible, adaptive, and efficient quantum sensing solutions in real-world scenarios.

DOI
10.1088/2058-9565/ae3acf

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Abstract Quantum optimal control in color center physics plays a crucial role in advancing sensor technology. This study focuses on optimizing microwave pulse shapes within a Ramsey sequence for nitrogen-vacancy centers to enhance sensor sensitivity and signal detection capabilities. We compare state-of-the-art optimization methods, including the dressed chopped randomized basis Nelder–Mead algorithm and covariance matrix adaptation evolutionary strategy, and extend our search to machine learning approaches, such as Gaussian processes and artificial neural networks. These machine learning techniques are specifically designed to provide robust and global solutions that can rapidly adapt to changing environmental conditions. Our results demonstrate more than a sixfold increase in convergence speed compared to conventional methods and considerable contrast improvements with a limited retraining set of 72 samples. Furthermore, we demonstrate that the optimized Ramsey contrast translates into a significant enhancement in the signal-to-noise ratio for detecting synthetic magnetic heart signals. This highlights the potential of machine learning-driven quantum optimal control for developing more flexible, adaptive, and efficient quantum sensing solutions in real-world scenarios.
Adaptive quantum computing control is important for the stability and efficiency of quantum systems, especially in the presence of dynamic environmental noise and hardware fluctuations. We present a unified strategy for machine learning (ML) techniques to improve adaptive quantum control in two main directions: optimization speed and out-of-sample error. Here, we propose a hybrid learning method combining reinforcement learning (RL), deep neural networks (DNNs), and quantum-inspired models to actively and adaptively update quantum gate parameters and error mitigation strategies on the fly. Embedding of quantum noise models directly into the learning allows the system to predict and correct for decoherence, gate errors and measurement inaccuracy before they affect computational fidelity. Using extensive simulations as well as experimental validation on noisy intermediate-scale quantum (NISQ) processors, our improved ML model achieves a substantially higher fidelity and robustness compared to standard model-free and static control approaches. We introduce a novel feedback-loop mechanism based on online learning algorithms enabling the system to adjust for changing noise profiles and hardware drift without retraining. The optimization part uses metaheuristic methods to quickly converge to control parameters that perform well, and the error prevention mechanism features anomaly detection and prediction correction layers to minimize the effects of cumulative decoherence. The results demonstrate that our adaptive control framework can be used to not only speed up the convergence in training quantum algorithms, but also sustain the performance of gates against error at a variety of error rates with the reduction of the real-time impact of the error up to 35 %. This work highlights the promise of machine learningaugmented quantum control for improving reliability and scalability in quantum computing and serves as a stepping stone for future incorporation with fault-tolerant designs.
Abstract Negatively charged nitrogen-vacancy (NV) centers in diamonds are commonly used in quantum magnetometry. However, the potential of this approach is often limited by the inhomogeneity of the driving field. In this study, we explore the potential of closed-loop quantum optimal control to improve DC Ramsey magnetometry with NV-ensembles suffering from inhomogeneous microwave (MW) fields and MW power limitations. We demonstrate an improvement of the optically detected Ramsey contrast up to a factor of 3.13. This enables noticeable power savings for miniaturized devices without loss in magnetometry performance. Additionally, we show a recovery of 86.3% of the Ramsey contrast in presence of a five times less homogeneous MW field compared to the homogeneous field of a MW Helmholtz coil pair.
Nitrogen-vacancy (NV) centers in diamond can serve as highly sensitive solid-state quantum sensors for high-sensitivity magnetometry. However, in the noisy intermediate-scale quantum (NISQ) era, extracting reliable information from noisy, finite-shot, and measurement-limited sensing data remains a considerable challenge. Whereas, quantum machine learning (QML) offers a potential path to improve parameter estimation by learning nonlinear relationships between quantum-sensing data and the underlying physical signal. In this work, we investigate the role of QML in magnetic-field estimation within an NV center-inspired magnetometry setting. We formulated magnetic field sensing as a supervised regression task. We compared the performance of several classical machine learning models trained on measurement-based classical data with that of quantum kernel-based models trained on pre-measurement coherent quantum states. Our objective is to isolate the impact of measurement-induced information loss and therefore provide a theoretical upper bound on the sensing performance. The upper bound is achievable only when coherent quantum information is directly available to the learning model. Our results show that QML-based sensing performance improves significantly with coherent quantum-state information, and not much with changes in model complexity or learning paradigm. This observation underscores the importance of learning pipelines that tightly integrate quantum sensors and QML models to enhance magnetic field sensing under realistic constraints.
Abstract Recent years have seen significant growth of quantum technologies, and specifically quantum sensing, both in terms of the capabilities of advanced platforms and their applications. One of the leading platforms in this context is nitrogen-vacancy (NV) color centers in diamond, providing versatile, high-sensitivity, and high-spatial-resolution magnetic sensing. Nevertheless, current schemes for spin resonance magnetic sensing (as applied by NV quantum sensing) suffer from tradeoffs associated with sensitivity, dynamic range, and bandwidth. Here we address this issue, and implement machine learning tools to enhance NV magnetic sensing in terms of the sensitivity/bandwidth tradeoff in large dynamic range scenarios. Our results indicate a potential reduction of required data points by at least a factor of 3, while maintaining the current error level. Our results promote quantum machine learning protocols for sensing applications towards more feasible and efficient quantum technologies.
Quantum magnetic sensing is a new paradigm for detecting ultra-weak magnetic fields. Its protocol design concerns the devise of optimal sensing parameters to estimate the underlying signals of interest (SoI). Existing studies mainly rely on adaptive algorithms based on black-box AI models or formula-driven principled searches. However, when the SoI spans a wide range and the quantum sensor has physical constraints, these methods may fail to converge efficiently or optimally, causing prolonged interrogation times and reduced accuracy. This work presents a new protocol using a two-stage optimization method, consisting of a Bayesian neural network to first narrow the SoI range and a federated reinforcement learning agent to subsequently calibrate the SoI. The protocol is developed and evaluated in the challenging context of an NV-center spin system with single-shot readout and limited sensing time; and yet it achieves significant improvements in both accuracy and resource efficiency compared to the state-of-the-art.
Shallow nitrogen-vacancy (NV) centers are promising candidates for high-precision sensing applications; these defects, when positioned a few nanometers below the surface, provide an atomic-scale resolution along with substantial sensitivity. However, the dangling bonds and impurities on the diamond surface result in a complex environment which reduces the sensitivity and is unique to each shallow NV center. To avoid the environment's detrimental effect, we apply feedback-based quantum optimal control. We first show how a direct search can improve the initialization and readout process. In a second step, we optimize microwave pulses for pulsed optically detected magnetic resonance (ODMR) and Ramsey measurements. Throughout the sensitivity optimizations, we focus on robustness against errors in the control field amplitude. This feature not only protects the protocols' sensitivity from drifts but also enlarges the sensing volume. The resulting ODMR measurements produce sensitivities below $1\phantom{\rule{0.28em}{0ex}}\ensuremath{\mu}\mathrm{T}$ ${\mathrm{Hz}}^{\ensuremath{-}\frac{1}{2}}$ for an 83% decrease in control power, increasing the robustness by approximately one third. The optimized Ramsey measurements produce sensitivities below 100 nT ${\mathrm{Hz}}^{\ensuremath{-}\frac{1}{2}}$ giving a twofold sensitivity improvement. Being on par with typical sensitivities obtained via single NV magnetometry, the complementing robustness of the presented optimization strategy may provide an advantage for other NV-based applications.