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.
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.
In modern-day quantum metrology, quantum sensors are widely employed to detect weak magnetic fields or nanoscale signals. Quantum devices, exploiting quantum coherence, are inevitably connected to physical constants and can achieve accuracy, repeatability, and precision approaching fundamental limits. As a result, these sensors have shown utility in a wide range of research domains spanning both science and technology. A rapidly emerging quantum sensing platform employs atomic-scale defects in crystals. In particular, magnetometry using nitrogen-vacancy (NV) color centers in diamond has garnered increasing interest. NV systems possess a combination of remarkable properties, optical addressability, long coherence times, and biocompatibility. Sensors based on NV centers excel in spatial resolution and magnetic sensitivity. These diamond-based sensors promise comparable combination of high spatial resolution and magnetic sensitivity without cryogenic operation. The above properties of NV magnetometers promise increasingly integrated quantum measurement technology, as a result, they have been extensively developed with various protocols and find use in numerous applications spanning materials characterization, nuclear magnetic resonance (NMR), condensed matter physics, paleomagnetism, neuroscience and living systems biology, and industrial vector magnetometry. In this chapter, NV centers are explored for magnetic sensing in a number of contexts. In general, we introduce novel regimes for magnetic-field probes with NV ensembles. Specifically, NV centers are developed for sensitive magnetometers for applications where microwaves (MWs) are prohibitively invasive and operations need to be carried out under zero ambient magnetic field. The primary goal of our discussion is to improve the utility of these NV center-based magnetometers.
Nitrogen Vacancy (NV) centers in diamond have emerged over the past few years as well-controlled quantum systems, with promising applications ranging from quantum information science to magnetic sensing. In this talk, I will describe new techniques for NV sensing – quantifying radical concentration and high-bandwidth compressed sensing. First, I will present our diamond magnetic microscope, enabling high-sensitivity and high-resolution magnetic sensing. I will demonstrate a novel technique we developed to characterize radical concentrations through their effect on the NVs (in collaboration with Uri Banin’s group). Then I will present a technique based on spectral compressed sensing, allowing high-bandwidth and large dynamic range magnetic sensing using NVs. We demonstrate the advantages of this approach and extend common compressed sensing schemes to practically “infinite resolution” in the frequency domain, further enhancing the the capabilities of our scheme.
Developing high performance portable sensors is of significant importance for both terrestrial and satellite communications, with quantum sensors offering potential advantages in sensitivity and bandwidth. The nitrogen vacancy center in diamond is a popular quantum sensing platform for its relative simplicity and wide range of acceptable operating conditions, but suffers a limited instantaneous bandwidth due to the narrow linewidth of the spin resonance. In this work, we first present a magnetic quantum sensing scheme utilizing heterodyne mixing with strong optical and microwave driving, achieving MHz-scale instantaneous bandwidth with a sensitivity of 548pT/Hz in an active diamond volume of $ 2.5\times 10^{-4} \; \text{mm}^3$ with 2×1011 NV centers. We then demonstrate expansion of the sensing bandwidth by employing a strong magnetic gradient and many individually modulated microwave control fields, expanding the single resonance bandwidth to 48 MHz with a peak sensitivity of 8.5nT/Hz.
Diamond quantum technologies based on color centers have rapidly emerged in the most recent years. The nitrogen-vacancy (NV) color center has attracted a particular interest, thanks to its outstanding spin properties and optical addressability. The NV center has been used to realize innovative multimode quantum-enhanced sensors that offer an unprecedented combination of high sensitivity and spatial resolution at room temperature. The technological progress and the widening of potential sensing applications have induced an increasing demand for performance advances of NV quantum sensors. Quantum control plays a key role in responding to this demand. This short review affords an overview on recent advances in quantum control-assisted quantum sensing and spectroscopy of magnetic fields.
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.
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.