  Let $\Omega_i\subset\mathbb{R}^{n_i}$, $i=1,\ldots,m$, be given domains. In
this article, we study the low-rank approximation with respect to
$L^2(\Omega_1\times\dots\times\Omega_m)$ of functions from Sobolev spaces with
dominating mixed smoothness. To this end, we first estimate the rank of a
bivariate approximation, i.e., the rank of the continuous singular value
decomposition. In comparison to the case of functions from Sobolev spaces with
isotropic smoothness, compare \cite{GH14,GH19}, we obtain improved results due
to the additional mixed smoothness. This convergence result is then used to
study the tensor train decomposition as a method to construct multivariate
low-rank approximations of functions from Sobolev spaces with dominating mixed
smoothness. We show that this approach is able to beat the curse of dimension.
