The first thing to check is the size and density of your matrix. The SYMMLQ function you mentioned is one of a group of methods for iteratively solving sparse linear systems (see doc), which should be used if your linear system is large and sparse (that is, around 1e4-by-1e4 at least to have a good likelyhood to be faster.
For a dense matrix, or for many sparse matrices, it's simpler and faster to use backslash. If A is real and exactly symmetric (issymmetric returns true), mldivide will use a symmetric algorithm if appropriate. You can symmetrize A in advance like Bruno suggests, which will backslash to always use a symmetric algorithm - in particular if your matrix is symmetric positive or negative definite, this is likely to help with performance since Cholesky is used in mldivide just as Torsten demonstrated.
