Random pulse layers boost quantum control with fewer parameters

Marco Dall’Ara and colleagues at the Fraunhofer Institute for Applied Solid State Physics IAF have developed new methods to streamline quantum control by constructing pulse sequences from random-amplitude pulses organized into layers, each governed by a single optimization parameter. The researchers demonstrate that increasing the number of these pulses leads to random unitaries converging exponentially fast, efficiently exploring the possibilities for controlling quantum states. This approach lowers the total number of parameters needed for optimization, and in gradient-free optimization, the RALLY methods are orders of magnitude more accurate with fewer evaluations. These findings offer a path toward advancing quantum machine learning and variational quantum algorithms. This convergence signifies an efficient exploration of the unitary space, a longstanding challenge in quantum optimal control. Two distinct methods, termed RALLYT and RALLYA, were developed to control these layers. RALLYT focuses on optimizing the…