A smoothing monotonic convergent optimal control algorithm for NMR pulse sequence design

TL;DR

This paper introduces a new smoothing monotonic optimal control algorithm for NMR pulse sequence design, enhancing stability and experimental implementability of pulse sequences in magnetic resonance applications.

Contribution

The paper presents a novel smoothing monotonic convergence algorithm that improves stability and smoothness in NMR pulse sequence optimization, facilitating experimental implementation.

Findings

Enhanced stability in pulse sequence optimization.

Produced smoother pulse sequences for easier experimental use.

Achieved faster convergence in the optimization process.

Abstract

The past decade has demonstrated increasing interests in using optimal control based methods within coherent quantum controllable systems. The versatility of such methods has been demonstrated with particular elegance within nuclear magnetic resonance (NMR) where natural separation between coherent and dissipative spin dynamics processes has enabled coherent quantum control over long periods of time to shape the experiment to almost ideal adoption to the spin system and external manipulations. This has led to new design principles as well as powerful new experimental methods within magnetic resonance imaging, liquid-state and solid-state NMR spectroscopy. For this development to continue and expand, it is crucially important to constantly improve the underlying numerical algorithms to provide numerical solutions which are optimally compatible with implementation on current instrumentation and at same time are numerically stable and offer fast monotonic convergence towards the target. Addressing such aims, we here present a smoothing monotonically convergent algorithm for pulse sequence design in magnetic resonance which with improved optimization stability lead to smooth pulse sequence easier to implement experimentally and potentially understand within the analytical framework of modern NMR spectroscopy.