Speaker
Description
Determining optimal time-dependent fields to steer quantum systems is a critical yet computationally demanding
task, often complicated by vast and complex search landscapes. This work explores the application
of tensor network methodologies to navigate these high-dimensional parameter spaces in quantum optimal
control effectively. We investigate how structured, low-rank tensor representations can be utilized for the
efficient parameterization and discovery of effective control trajectories. Our approach involves an iterative
refinement process where the tensor network model is adaptively updated based on simulated quantum system
performance. This allows for a guided exploration of the control landscape, aiming to identify highly
effective control strategies. The potential benefits include enhanced search efficiency and the ability to tackle
complex control problems relevant to emerging quantum technologies.
