Quantum sensors leverage matter's quantum properties to enable measurements with unprecedented spatial and spectral resolution. Among these sensors, those utilizing nitrogen-vacancy (NV) centers in diamond offer the distinct advantage of operating at room temperature. Nevertheless, signals received from NV centers are often complex, making interpretation challenging. This is especially relevant in low magnetic field scenarios, where standard approximations for modeling the system fail. Additionally, NV signals feature a prominent noise component. In this Letter, we present a signal-to-image deep learning model capable of automatically inferring the number of nuclear spins surrounding a NV sensor and the hyperfine couplings between the sensor and the nuclear spins. Our model is trained to operate effectively across various magnetic field scenarios, requires no prior knowledge of the involved nuclei, and is designed to handle noisy signals, leading to fast characterization of nuclear environments in real experimental conditions. With detailed numerical simulations, we test the performance of our model in scenarios involving varying numbers of nuclei, achieving an average error of less than 2 kHz in the estimated hyperfine constants.
Quantum sensors leverage matter's quantum properties to enable measurements with unprecedented spatial and spectral resolution. Among these sensors, those utilizing nitrogen-vacancy (NV) centers in diamond offer the distinct advantage of operating at room temperature. Nevertheless, signals received from NV centers are often complex, making interpretation challenging. This is especially relevant in low magnetic field scenarios, where standard approximations for modeling the system fail. Additionally, NV signals feature a prominent noise component. In this Letter, we present a signal-to-image deep learning model capable of automatically inferring the number of nuclear spins surrounding a NV sensor and the hyperfine couplings between the sensor and the nuclear spins. Our model is trained to operate effectively across various magnetic field scenarios, requires no prior knowledge of the involved nuclei, and is designed to handle noisy signals, leading to fast characterization of nuclear environments in real experimental conditions. With detailed numerical simulations, we test the performance of our model in scenarios involving varying numbers of nuclei, achieving an average error of less than 2 kHz in the estimated hyperfine constants.
The recent decade has witnessed exciting developments in quantum science and technology. Quantum sensing is a rapidly growing branch, which exploits the fragility of quantum systems to detect small external signals. The extreme control at the single-atom level and the remarkably long coherence time of engineered quantum systems enable unprecedented sensitivity and precision, beating the performance of classical techniques. The nitrogen-vacancy (NV) center in diamond is one of the most promising quantum sensors. Its unique advantages have attracted considerable attention, including a long spin coherence time throughout a broad temperature range, nanoscale resolution limited by the sensor-sample distance, convenient optical control of its spin states and remarkably versatile sensing capabilities. Its properties are well studied under a bias magnetic field oriented along its quantization axis. However, its sensing capabilities in the presence of an off-axis magnetic field are much less explored. My research aims to unlock the full potential of NV quantum sensor by developing new sensing modalities under a magnetic field acting perpendicular to the NV axis, and furthermore, integrating them into a scanning probe microscope. In this dissertation, I will report our progress toward building a scanning NV multimodal quantum imaging platform, which will open up exciting opportunities in measuring nanoscale phenomena. The first chapter provides an introduction to the background of quantum sensing with NV centers, followed by a discussion on its basic properties and established sensing techniques. The second and third chapters are dedicated to two experiments performed under a perpendicular magnetic field where traditional NV magnetometry based on the Zeeman effect fails. In the second chapter, I will introduce a new sensing strategy that uses the entanglement between the electron and nuclear spins to restore the magnetic field sensitivity under this unfavored condition. This allows us to sense small changes in the magnetic field angle relative to the NV axis, and to detect anisotropic magnetic noise in the local environment. In the third chapter, I will present our latest development of scanning NV electrometry, where a single NV at the apex of a diamond scanning probe images external alternating (AC) and direct (DC) electric fields with sub-100 nm resolution. In the last chapter, I will discuss our efforts on four experiments that were left unfinished. I will present the results we have achieved, analyze the challenges that we faced and propose potential solutions to overcome them.
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.
Precise characterization of the local spin environment of single diamond nitrogen-vacancy (NV) centers is crucial for advancing quantum sensing, quantum networking, and the optimization of quantum materials. However, single NV center fluorescence measurements requires long averaging times to obtain clean data that is suitable for conventional model fitting, and that constitutes a key experimental bottleneck for high-throughput characterization. To address this, we introduce \textsc{NVRNet}, a physics-informed simulation-to-reality machine learning pipeline that maps minimal-sweep, noisy Ramsey data to a denoised waveform while directly estimating the hyperfine coupling to proximal 13C nuclear spins. The pipeline's denoiser utilizes a two-stage time-frequency U-Net and an attention-augmented time-domain U-Net, pretrained on Hamiltonian-based spin-dynamics simulations with experimentally calibrated noise. To effectively bridge the simulation-to-reality gap, parameter-efficient adapters are attached to the backbone and fine-tuned on targeted experimental data. Across three distinct NV centers, this experimentally fine-tuned model reduces the median reconstruction error on held-out, few-sweep traces to 0.44-0.67× of the raw experimental noise level. Subsequently, a transformer-based estimator extracts the underlying hyperfine parameters. Forward reconstructions derived from these inferred parameters faithfully reproduce the dominant experimental time- and frequency-domain features, yielding representative normalized fast Fourier transform (FFT) reconstruction errors of 0.10-0.19. By reducing both the required data volume and acquisition time, \textsc{NVRNet} enables up to ∼40× acceleration of the measurement process, establishing a fast, hardware-compatible pathway for robust hyperfine inference and autonomous qubit characterization.
Diamond-based quantum sensors have enabled high-resolution NMR spectroscopy at the microscale in scenarios where fast molecular motion averages out dipolar interactions among target nuclei. However, in samples with low-diffusion, ubiquitous dipolar couplings challenge the extraction of relevant spectroscopic information. In this work we present a protocol that enables the scanning of nuclear spins in dipolarly-coupled samples at high magnetic fields with a sensor based on nitrogen vacancy (NV) ensembles. Our protocol is based on the synchronized delivery of radio frequency (RF) and microwave (MW) radiation to eliminate couplings among nuclei in the scanned sample and to efficiently extract target energy-shifts from the sample's magnetization dynamics. In addition, the method is designed to operate at high magnetic fields leading to a larger sample thermal polarization, thus to an increased NMR signal. The precision of our method is ultimately limited by the coherence time of the sample, allowing for accurate identification of relevant energy shifts in solid-state systems.
Quantum sensing has witnessed rapid development and transition from laboratories to practical applications in the past decade. Applications of quantum sensors, ranging from nanotechnologies to biosensing, are expected to benefit from quantum sensors' unprecedented spatial resolution and sensitivity. Solid-state spin systems are particularly attractive platforms for quantum sensing technologies because room temperature operation is viable while reaching the quantum limits of sensitivity. Among various solid-state spin systems, nitrogen-vacancy (NV) centers in diamond demonstrated high-fidelity initialization, coherent control, and high contrast readout of the electron spin state. However, electron spin coherence due to noise from the surrounding spin bath and this environment effect limits the sensitivity of NV centers. Thus, a critical task in NV center-based quantum sensing is sensitivity enhancement through coherence protection. Several strategies, such as dynamical decoupling techniques, feedback control, and nuclear spin-assisted sensing protocols, have been developed and realized for this task. Among these strategies, nuclear spin-assisted protocols have demonstrated greater enhancement of electron spin coherence. In addition, the electron and nuclear spin pair of an NV center in diamond naturally allows the application of the nuclear spin-assisted sensitivity enhancement protocol. Owing to long nuclear coherence times, further enhancement of sensitivity can be achieved by exploiting active nuclear spins (e.g., 14N, 13C) in the proximity of an NV center as memory ancillas when coupled with the NV center. Here, we review the spin properties of NV centers, mechanisms of the nuclear spin-assisted protocol and its gate variation, and the status of quantum sensing applications in high-resolution nuclear spin spectroscopy, atomic imaging, and magnetic field sensing. We discuss the potential for commercialization, current challenges in sensitivity enhancement, and conclude with future research directions for promoting the development of nuclear spin-assisted protocol and its integration into industrial applications.
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.