Spin Dynamics, Pulsed Decoupling, and Deep Reinforcement Learning for Quantum Sensing

Abstract

Quantum sensing faces challenges in moving from proof-of-concept demonstrations to practical technologies, with discrepancies between idealized theoretical frameworks and experimental realities presenting a key obstacle. This dissertation explores these nuances — with a focus on nitrogen-vacancy (NV) centers in diamond — through four projects spanning experiment, theory, simulations, and algorithmic optimization.

We characterize Ramsey envelope modulation effects in 15NV diamond magnetometry, showing that magnetic field misalignments produce envelope effects that degrade sensitivity. Next, we study the breakdown of Average Hamiltonian Theory (AHT) in experimental regimes, introduce exact methods to calculate a sensor response to a target signal that are valid beyond AHT, and establish symmetries that guarantee AHT convergence. With Ensemble Cluster Sampling (ECS), we address overfitting in dynamical decoupling by training algorithms on heterogeneous parameter distributions rather than idealized systems. Finally, we present TEMPO, an open-source Python package for accessible pulse sequence simulations.

These studies underline the need for continued collaboration between quantum sensing theory and laboratory applications, while maintaining an eye on modern algorithms and software.