Acceleration of quantum optimal control theory algorithms with mixing strategies

TL;DR

This paper introduces mixing strategies to speed up iterative algorithms in Quantum Optimal Control Theory by viewing the problem as a fixed-point non-linear problem, demonstrating significant acceleration through numerical examples.

Contribution

The paper applies mixing strategies from density functional theory to quantum control algorithms, providing a novel approach to enhance convergence speed.

Findings

Mixing strategies significantly accelerate QOCT algorithms.

Numerical examples confirm improved convergence rates.

Approach bridges methods from density functional theory and quantum control.

Abstract

We propose the use of mixing strategies to accelerate the convergence of the common iterative algorithms utilized in Quantum Optimal Control Theory (QOCT). We show how the non-linear equations of QOCT can be viewed as a "fixed-point" non-linear problem. The iterative algorithms for this class of problems may benefit from mixing strategies, as it happens, e.g. in the quest for th ground-state density in Kohn-Sham density functional theory. We demonstrate, with some numerical examples, how the same mixing schemes utilized in this latter non-linear problem, may significantly accelerate the QOCT iterative procedures.