With the second quantum revolution underway, quantum-enhanced sensors are moving from laboratory demonstrations to field deployments, providing enhanced and even new capabilities. Signal processing and operational software are becoming integral parts of these emerging sensing systems to reap the benefits of this progress. This letter looks into widefield nitrogen vacancy (NV) center-based magnetometry and focuses on estimating the magnetic field from the optically detected magnetic resonances (ODMR) signal, a crucial output for various applications. Mapping the shifts of ODMR signals to phase estimation, a computationally efficient approaches are proposed. Involving Fourier transform (FT) and filtering as preprocessing steps, the suggested approaches involve linear curve fit or complex frequency estimation based on well known super-resolution technique estimation of signal parameters via rotational invariant techniques (ESPRIT). The existing methods in the quantum sensing literature take different routes based on Lorentzian fitting for determining magnetic field maps. To showcase the functionality and effectiveness of the suggested techniques, relevant results, based on experimental data are provided, which shows a significant reduction in computational time with the proposed method over existing methods.
With the second quantum revolution underway, quantum-enhanced sensors are moving from laboratory demonstrations to field deployments, providing enhanced and even new capabilities. Signal processing and operational software are becoming integral parts of these emerging sensing systems to reap the benefits of this progress. This letter looks into widefield nitrogen vacancy (NV) center-based magnetometry and focuses on estimating the magnetic field from the optically detected magnetic resonances (ODMR) signal, a crucial output for various applications. Mapping the shifts of ODMR signals to phase estimation, a computationally efficient approaches are proposed. Involving Fourier transform (FT) and filtering as preprocessing steps, the suggested approaches involve linear curve fit or complex frequency estimation based on well known super-resolution technique estimation of signal parameters via rotational invariant techniques (ESPRIT). The existing methods in the quantum sensing literature take different routes based on Lorentzian fitting for determining magnetic field maps. To showcase the functionality and effectiveness of the suggested techniques, relevant results, based on experimental data are provided, which shows a significant reduction in computational time with the proposed method over existing methods.
Quantum sensing using nitrogen-vacancy (NV) centers in diamond has emerged as a powerful platform for detecting ultra-low magnetic fields under ambient conditions. Owing to their long spin coherence times, optical addressability, and compatibility with aqueous environments, it has found widespread applications in biosensing and bio-imaging. This review presents the fundamental principles and recent advances in NV-based quantum magnetometry for biosensing applications, with a particular focus on measurements in aqueous medium and at cellular and molecular length scales. We discuss the underlying spin physics of NV centers and highlight two primary detection modalities: optically detected magnetic resonance (ODMR) and T1 relaxometry-based sensing and how these approaches aids in the detection of both static magnetic fields and dynamic magnetic noise arising from biological processes. The review explores key application areas, including nanoscale nuclear magnetic resonance (NMR), monitoring of neural activity, detection of abnormal or rogue cells using NV-based platforms etc. In addition, strategies for enhancing sensitivity, such as surface functionalization of nanodiamonds, femtosecond (fs) laser-written photonic structures, and integration with microfluidic and lab-on-chip systems have also been discussed in depth. We also address the critical challenges, including surface-induced decoherence, charge-state instability, and signal-to-noise limitations in biofluids associated with NV-based biosensing applications. Finally, we outline future prospects, highlighting how NV-based magnetic biosensing provides a promising pathway for translating quantum sensing technologies into practical biomedical applications.
Many spintronic, magnetic-memory, and neuromorphic devices rely on spatially varying magnetic fields. Quantitatively imaging these fields with full vector information over extended areas remains a major challenge. Existing probes either offer nanoscale resolution at the cost of slow scanning, or widefield imaging with limited vector sensitivity or material constraints. Quantum sensing with nitrogen-vacancy (NV) centers in diamond promises to bridge this gap, but a practical camera-based vector magnetometry implementation on relevant microstructures has not been demonstrated. Here we adapt a commercial widefield microscope to implement a camera-compatible pulsed optically detected magnetic resonance protocol to reconstruct stray-field vectors from microscale devices. By resolving the Zeeman shifts of the four NV orientations, we reconstruct the stray-field vector generated by microfabricated permalloy structures that host multiple stable remanent states. Our implementation achieves a spatial resolution of ≈0.52μm across an 83μm×83μm field of view and a peak sensitivity of $ (828 \pm 142)~\mathrm{nT\,Hz^{-1}}$, with acquisition times of only a few minutes. These results establish pulsed widefield NV magnetometry on standard microscopes as a practical and scalable tool for routine vector-resolved imaging of complex magnetic devices.
The nitrogen-vacancy (NV) center in diamond has become a widely used platform for quantum sensing. The four NV axes in monocrystalline diamond specifically allow for vector magnetometry, with magnetic-field sensitivities reaching down to fT / Hz . The current literature primarily focuses on improving the precision of NV-based magnetometers. Here, we study the experimental accuracy of determining the magnetic field from measured spin-resonance frequencies via solving the NV Hamiltonian. We derive exact, analytical, and fast-to-compute formulas for calculating resonance frequencies from a known magnetic-field vector, and vice versa (formulas for calculating the magnetic-field vector from measured resonance frequencies). Additionally, the accuracy of often-used approximations is assessed. Finally, we promote using the Voigt profile as a fit model to determine the linewidth of measured resonances accurately. An open-source Python package accompanies our analysis.
This thesis investigates the theoretical foundations, material properties, and experimental realization of quantum sensors based on the Nitrogen-Vacancy (NV) centers in diamond, a solid-state system that combines quantum coherence with room-temperature operability. The first chapter introduces the principles of quantum sensing, outlining the fundamental definitions, operating protocols, and physical processes underlying the quantum measurement paradigm. Emphasis is placed on the quantum sensing Hamiltonian and the distinction between various classes of quantum sensors. The second chapter focuses on diamond as a host material and on the NV center as a quantum defect. The electronic structure, optical transitions, and spin properties of the NV center are analyzed in detail. Several techniques of NV-based magnetometry are presented, including continuouswave and pulsed optically detected magnetic resonance (ODMR). The discussion also addresses coherence times (T1, T2, and T∗2 ) and methods of diamond synthesis together with NV-center generation through irradiation and annealing. Chapter three develops the physical theory describing the NV center. The Hamiltonian formalism includes spin-spin, hyperfine, and quadrupolar interactions, as well as couplings to external magnetic fields. The chapter further introduces the Bloch sphere representation, magnetic resonance phenomena, and models of lightmatter interaction, ranging from the semiclassical to the fully quantum Rabi framework. A seven-level rate-equation model is proposed to capture the spin dynamics and optical cycling processes of the NV center. The fourth chapter reports the experimental results. After describing materials and methods, several ODMR-based vector magnetometry experiments are presented, highlighting different spectral regimes and the dependence of resonance features on the external magnetic field geometry. Additional studies demonstrate temperature sensing and microwave-free magnetometry techniques for mapping magnetic field distributions. Sensitivity analysis and calibration procedures are also discussed.
Nitrogen-vacancy (NV) centers in diamond are a versatile quantum sensing platform for high sensitivity measurements of magnetic fields, temperature and strain with nanoscale spatial resolution. A common bottleneck is the analysis of optically detected magnetic resonance (ODMR) spectra, where target quantities are encoded in resonance features. Conventional nonlinear fitting is often computationally expensive, sensitive to initialization, and prone to failure at low signal-to-noise ratio (SNR). Here we introduce a robust, efficient machine learning (ML) framework for real-time ODMR analysis based on a one-dimensional convolutional neural network (1D-CNN). The model performs direct parameter inference without initial guesses or iterative optimization, and is naturally parallelizable on graphics processing units (GPU) for high-throughput processing. We validate the approach on both synthetic and experimental datasets, showing improved throughput, accuracy and robustness than standard nonlinear fitting, with the largest gains in the low-SNR regime. We further validate our methods in two representative sensing applications: diagnosing intracellular temperature changes using nanodiamond probes and widefield magnetic imaging of superconducting vortices in a high-temperature superconductor. This deep-learning inference framework enables fast and reliable extraction of physical parameters from complex ODMR data and provides a scalable route to real-time quantum sensing and imaging.
The field of quantum sensing has shown rapid advancement in recent years. These advances have focused on making quantum sensors, which previously required laboratory environments and expensive bulky equipment, more portable and cost-effective. These developments are largely due to the use of nitrogen-vacancy (NV) centers in diamond. NV center diamond is suitable because its quantum mechanical properties can be observed over a wide range of temperatures and do not require strictly controlled conditions such as environmental shielding. These characteristics make NV centers promising candidates for sensing applications, such as defense-related technologies. Potential applications include magnetometry for navigation and radio-frequency sensing for signal interception, motivating the project conducted at the Swedish Defence Research Agency (FOI). This thesis presents and evaluates a low-cost and portable design of an NV center based magnetometer to determine the feasibility of such designs. The results demonstrate that the sensor is capable of detecting the underlying quantum phenomena in a diamond containing NV centers, yielding an optically detected magnetic resonance (ODMR) signal. Furthermore, the sensor responds to changes in magnetic fields. However, several design challenges remain before achieving high-accuracy sensing performance.