Speaker
Description
We present a family of discrete optimal control problems that are motivated by quantum pulse optimization to design quantum gates. We solve the continuous relaxation using the gradient ascent pulse engineering (GRAPE) algorithm, and apply combinatorial integral approximation (CIA) techniques to obtain discrete optimal controls. To add constraints and more complex regularization terms, we develop an alternating direction of multiplier (ADMM) method. We show empirically that ADMM improves the rounding results of CIA compared to GRAPE. Time permitting, we also comment on methods and models that can optimize under uncertainty, and show recent results that indicate that taking uncertainty into account can be critical for optimal control.
Joint work with: Xinju Fei (University of Michigan), Siqian Chen
(University of Michigan), Jeff Larson (Argonne National Laboratory).
