Nitrogen-vacancy (NV) centers in diamond are a solid-state platform for quantum sensing 1 and information processing due to their spin-dependent optical transitions at room temperature 2. Accurate characterization of NV spin dynamics, including hyperfine interactions with nuclear spins, is essential both for optimizing sensing performance and for designing high-fidelity quantum controls. In this work, we use the open-source package/ library Quantum Toolbox in Python (QuTiP) 3, 4 to simulate two readout protocols (continuous-wave ODMR, pulsed ODMR) with and without the hyperfine coupling. By comparing simulated resonance positions and population dynamics against experimental and simulation profiles from the literature, we demonstrate that our QuTiP model reproduces the characteristic profiles present in other works. We analyze the computational cost of including hyperfine terms to the model via execution time averaging and show that built-in parallelization accelerates the no-hyperfine model but provides small returns when the coupling is included. Finally, we illustrate how simulation parameters align with predictions of DC magnetometry 5. We also provide some ideas for extending this framework to GPU-accelerated and/or machine learning assisted parameter estimation and calibration 6, 7. Our soon-to-be open-source code 8 provides a flexible foundation for both quantum sensing characterization and the development of NV-based quantum information protocols.
Nitrogen-vacancy (NV) centers in diamond are a solid-state platform for quantum sensing 1 and information processing due to their spin-dependent optical transitions at room temperature 2. Accurate characterization of NV spin dynamics, including hyperfine interactions with nuclear spins, is essential both for optimizing sensing performance and for designing high-fidelity quantum controls. In this work, we use the open-source package/ library Quantum Toolbox in Python (QuTiP) 3, 4 to simulate two readout protocols (continuous-wave ODMR, pulsed ODMR) with and without the hyperfine coupling. By comparing simulated resonance positions and population dynamics against experimental and simulation profiles from the literature, we demonstrate that our QuTiP model reproduces the characteristic profiles present in other works. We analyze the computational cost of including hyperfine terms to the model via execution time averaging and show that built-in parallelization accelerates the no-hyperfine model but provides small returns when the coupling is included. Finally, we illustrate how simulation parameters align with predictions of DC magnetometry 5. We also provide some ideas for extending this framework to GPU-accelerated and/or machine learning assisted parameter estimation and calibration 6, 7. Our soon-to-be open-source code 8 provides a flexible foundation for both quantum sensing characterization and the development of NV-based quantum information protocols.
The nitrogen-vacancy (NV) center in diamond is a leading solid-state platform for room-temperature quantum magnetometry owing to its long spin coherence times, optical spin initialization and readout, and high sensitivity to magnetic, electric, and thermal perturbations. As NV-based optically detected magnetic resonance (ODMR) systems transition from controlled laboratory environments toward portable and field-deployable sensors, a detailed understanding of realistic noise sources and experimental imperfections becomes essential for optimizing performance and sensitivity. In this work, we present a comprehensive simulation framework, i.e., a digital twin, for continuous-wave wide-field ODMR in NV-center ensembles. The model is built upon a physically consistent seven-level description of the NV center and incorporates a broad range of experimentally relevant noise and imperfection mechanisms as modular, parameterized components. These include laser and microwave amplitude fluctuations, microwave phase noise, uncertainty in the NV gyromagnetic ratio, spin dephasing, temperature-induced shifts of the ground-state zero-field splitting, surface-induced magnetic field perturbations, and photon shot noise. Power broadening and contrast degradation arising from optical and microwave driving are captured self-consistently through linewidth calculations. Also, the spatial inhomogeneity is modeled via a Gaussian laser intensity profile across the sensing region...
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.
Nitrogen vacancy (NV-) color centers in diamond are a prime candidate for use in quantum information devices, owing to their spin-1 ground state, straightforward optical initialization and readout, and long intrinsic coherence times in a room-temperature solid. While the 13C nuclear spin is often a dominant source of magnetic noise, we observe transitions between electron-nuclear hyperfine states of NV- centers in 99% 13C diamond that are robust to decoherence. At magnetic field strengths ranging from 550 - 900 G, these transitions are observable by optically detected magnetic resonance (ODMR), and exhibit linewidths narrowed by factors as high as ~130 at room temperature over typical electron-type transitions observed from this spin system. We anticipate the use of these decoherence-protected transitions, in combination with dynamical decoupling methods, for storage of quantum information.
Abstract The decoherence of point defect qubits is often governed by the electron spin-nuclear spin hyperfine interaction that can be parameterized by using ab inito calculations in principle. So far most of the theoretical works have focused on the hyperfine interaction of the closest nuclear spins, while the accuracy of the predictions for distinct nuclear spins is barely discussed. Here we demonstrate for the case of the NV center in diamond that the absolute relative error of the computed hyperfine parameters can exceed 100% using an industry standards first-principles code. To overcome this issue, we implement an alternative method and report on significantly improved hyperfine values with O O (1%) relative mean error at all distances. The provided accurate hyperfine data for the NV center enables high-precision simulation of NV quantum nodes for quantum information processing and positioning of nuclear spins by comparing experimental and theoretical hyperfine data.
ABSTRACT The nitrogen‐vacancy (NV) center in diamond is a crucial platform for quantum technologies, where its precise numerical modeling is indispensable for the continued advancement of the field. We present here a Python library for simulating the NV spin dynamics under general experimental conditions, i.e. a digital twin. Our library accounts for electromagnetic pulses and other environmental inputs, which are used to solve the system's time evolution, resulting in a physical output in the form of a quantum observable given by fluorescence. The simulation framework is based on a non‐perturbative time‐dependent Hamiltonian model, where the states initialization and readout are postulated from the interaction with optical fields. By eliminating oversimplifications such as the adoption of rotating frames for the microwave and radio frequency fields, our simulations reveal subtle dynamics emerging from realistic pulse constraints. The software is illustrated with three examples and validated by comparing the simulations with experimental reports, relevant to the fields of quantum computing (conditional logic gates), sensing (dynamical decoupling sequences with coupled spins) and networks (state teleportation). Overall, this digital twin delivers a robust numerical modeling of the NV spin dynamics, with simple and accessible usability, which can be used for a wide range of applications.