  A well-known approach in the design of efficient algorithms, called matrix
sparsification, approximates a matrix $A$ with a sparse matrix $A'$. Achlioptas
and McSherry [2007] initiated a long line of work on spectral-norm
sparsification, which aims to guarantee that $\|A'-A\|\leq \epsilon \|A\|$ for
error parameter $\epsilon>0$. Various forms of matrix approximation motivate
considering this problem with a guarantee according to the Schatten $p$-norm
for general $p$, which includes the spectral norm as the special case
$p=\infty$.
  We investigate the relation between fixed but different $p\neq q$, that is,
whether sparsification in the Schatten $p$-norm implies (existentially and/or
algorithmically) sparsification in the Schatten $q\text{-norm}$ with similar
sparsity. An affirmative answer could be tremendously useful, as it will
identify which value of $p$ to focus on. Our main finding is a surprising
contrast between this question and the analogous case of $\ell_p$-norm
sparsification for vectors: For vectors, the answer is affirmative for $p<q$
and negative for $p>q$, but for matrices we answer negatively for almost all
sufficiently distinct $p\neq q$. In addition, our explicit constructions may be
of independent interest.
