A genetic algorithm to generate maximally orthogonal frames in complex space - Sebastián Roca-Jerat; Juan Román-Roche (Machine Learning: Science and Technology)
A frame is a generalization of a basis of a vector space to a redundant overspanning set whose vectors are linearly dependent. Frames find applications in signal processing and quantum information theory. We present a genetic algorithm that can generate maximally orthogonal frames (MOFs) of arbitrary size n in d -dimensional complex space. First, we formalize the concept of MOF and demonstrate that it depends on the choice of an energy function to weigh the different pairwise overlaps between vectors. Then, we discuss the relation between different energy functions and well-known frame varieties such as tight and Grassmannian frames and complex projective p -designs. Obtaining MOFs poses a global non-convex minimization problem. We discuss the relation with established numerical problems such as the Thomson problem and the problem of finding optimal packings in complex projective space. To tackle the minimization, we design a hybrid genetic algorithm that features local optimization of the parents. To assess the performance of the algorithm, we propose two visualization techniques that allow us to analyze the coherence and uniformity of high-dimensional frames. The genetic algorithm is able to produce highly-symmetric universal frames, such as equiangular tight frames, symmetric, informationally complete, positive operator-valued measurements and maximal sets of mutually unbiased bases, for configurations of up to d  = 6 and n  = 36, with runtimes of the order of several minutes on a regular desktop computer for the largest configurations.