  Homogenization in terms of multiscale limits transforms a multiscale problem
with $n+1$ asymptotically separated microscales posed on a physical domain $D
\subset \mathbb{R}^d$ into a one-scale problem posed on a product domain of
dimension $(n+1)d$ by introducing $n$ so-called "fast variables". This
procedure allows to convert $n+1$ scales in $d$ physical dimensions into a
single-scale structure in $(n+1)d$ dimensions. We prove here that both the
original, physical multiscale problem and the corresponding high-dimensional,
one-scale limiting problem can be efficiently treated numerically with the
recently developed quantized tensor-train finite-element method (QTT-FEM).
  The method is based on restricting computation to sequences of nested
subspaces of low dimensions (which are called tensor ranks) within a vast but
generic "virtual" (background) discretization space. In the course of
computation, these subspaces are computed iteratively and data-adaptively at
runtime, bypassing any "offline precomputation". For the purpose of theoretical
analysis, such low-dimensional subspaces are constructed analytically to bound
the tensor ranks vs. error $\tau>0$.
  We consider a model linear elliptic multiscale problem in several physical
dimensions and show, theoretically and experimentally, that both (i) the
solution of the associated high-dimensional one-scale problem and (ii) the
corresponding approximation to the solution of the multiscale problem admit
efficient approximation by the QTT-FEM. These problems can therefore be
numerically solved in a scale-robust fashion by standard (low-order) PDE
discretizations combined with state-of-the-art general-purpose solvers for
tensor-structured linear systems. We prove scale-robust exponential
convergence, i.e., that QTT-FEM achieves accuracy $\tau$ with the number of
effective degrees of freedom scaling polynomially in $\log \tau$.
