Exploiting Landscape Geometry to Enhance Quantum Optimal Control

Abstract

The successful application of Quantum Optimal Control (QOC) over the past decades unlocked the possibility of directing the dynamics of quantum systems. Nevertheless, solutions obtained from QOC algorithms are usually highly irregular, making them unsuitable for direct experimental implementation. In this paper, we propose a method to reshape those unattractive optimal controls. The approach is based on the fact that solutions to QOC problems are not isolated policies but constitute multidimensional submanifolds of control space. This was originally shown for finite-dimensional systems. Here, we analytically prove that this property is still valid in a continuous variable system. The degenerate subspace can be effectively traversed by moving in the null subspace of the hessian of the cost function, allowing for the pursuit of secondary objectives. To demonstrate the usefulness of this procedure, we apply the method to smooth and compress optimal protocols in order to meet laboratory demands.

8 pages, 2 figures

References in corpus (5)

• Probing many-body dynamics on a 51-atom quantum simulator
• Single ion heat engine with maximum efficiency at maximum power
• Trap-free manipulation in the Landau-Zener system
• Comparison between optimal control and shortcut to adiabaticity protocols in a linear control system
• Optimal quantum control with digitized Quantum Annealing

Cited by in corpus (2)

• Connection between inverse engineering and optimal control in shortcuts to adiabaticity
• Nonadiabatic energy fluctuations of scale-invariant quantum systems in a time-dependent trap