  It is often of interest to identify a given number of points in projective
space such that the minimum distance between any two points is as large as
possible. Such configurations yield representations of data that are optimally
robust to noise and erasures. The minimum distance of an optimal configuration
not only depends on the number of points and the dimension of the projective
space, but also on whether the space is real or complex. For decades, Neil
Sloane's online Table of Grassmannian Packings has been the go-to resource for
putatively or provably optimal packings of points in real projective spaces.
Using a variety of numerical algorithms, we have created a similar table for
complex projective spaces. This paper surveys the relevant literature, explains
some of the methods used to generate the table, presents some new putatively
optimal packings, and invites the reader to competitively contribute
improvements to this table.
