Optimizing continuous dynamical decoupling with machine learning

Abstract

Decoherence of a quantum system is one of the main difficulties for quantum information processing. Continuous dynamical decoupling has achieved great success in the improvement of the coherence of a quantum state but is difficult to optimize. Here we exploit the black-box optimization process in a machine learning algorithm to optimize the dynamical decoupling scheme. By applying the discrete optimization process to continuous driving fields, we achieve a longer coherence time in comparison to representative schemes of dynamical decoupling. Our black-box-optimization-based machine learning algorithm provides a general routine to tackle the challenging task of improving the coherence of a quantum state for which the dephasing of the system is crucial for operation.