Many engineering optimization problems may be rephrased in terms of equivalent binary problems, and these can be effectively tackled with Evolutionary Algorithms. Unfortunately, when dealing with antenna designs, the fitness function computation may be extremely time consuming and therefore it is of paramount importance to speed up the convergency and to improve the performances of this kind of algorithms. The recent introduction and the increasing availability of quantum computing may be very effective to accelerate the design process, even though new approaches and new algorithms are needed in order to exploit the specificity of these instruments. In this paper, a new version of a novel quantum crossover operator for binary Genetic Algorithm (bGA) has been introduced and compared with its previous version. It has been successfully tested on different mathematical benchmark functions and on a preliminary thinned array design.