Abstract: Nitrogen-Vacancy (NV) centers in diamond hold transformative potential in quantum technologies, leveraging their unique spin-optical properties, long coherence times, and room-temperature operation. However, challenges such as inefficient photon detection and bulky experimental setups hinder their broader deployment. This thesis addresses these issues through advancements in theoretical modeling, experimental design, and computational integration of NV centers.

A key contribution is a novel framework for modeling Photon Emission Statistics (PES) in NV centers. Building on the quantum jump formalism, this model improves upon traditional rate-equation approaches by explicitly linking photon emission streams to NV quantum states. This detailed understanding enables the optimization of quantum sensing protocols, particularly for magnetic field measurements, by enhancing sensitivity and efficiency. The PES framework also supports simulations of photophysical properties, facilitating analysis of photon arrival times and autocorrelation functions.

To transition NV-based systems from laboratories to real-world applications, the thesis introduces a compact confocal microscope for optical initialization and coherent control of NV states. This miniaturized system addresses size, complexity, and cost barriers, broadening accessibility and enabling deployment in diverse environments.

The integration of NV centers into Quantum Machine Learning (QML) is another focus, exploring their role in quantum neural network (QNN) architectures. NV centers are shown to enable high-fidelity qubit operations and entangling gates, enhancing QNNs’ ability to perform complex regression tasks. The analysis reveals how network depth, qubit count, and entanglement influence QNN performance, underscoring the potential of NV centers in quantum-enhanced computation.

These advancements are supported by theoretical, experimental, and computational validations. The PES framework improves NV center characterization, the portable microscope achieves efficient NV control, and the QNN research demonstrates NV center utility in advanced computational frameworks.

Looking forward, this work lays the foundation for several promising directions. Future research could focus on improving photon detection efficiency to further enhance sensing performance. Additionally, scaling NV center-based systems for use in quantum networks remains a critical challenge that warrants continued investigation. The integration of NV centers into scalable quantum machine learning models also offers significant potential for advancing both fields. By addressing these challenges, the findings of this thesis will help to unlock the full potential of NV centers in quantum technologies, bridging the gap between fundamental research and practical applications.