With the use of the diagonal operator $\operatorname{diag}(\mathbf x)$, which gives the diagonal matrix whose diagonal has the entries of $\mathbf x$ on it, we can write
$$
\begin{bmatrix}1 & 1 & 0 & 0 \\ 0 & 0 & 1 & 1\end{bmatrix} \operatorname{diag}(a,b,c,d) \begin{bmatrix}1 & 0 \\ 0 & 1 \\ 1 & 0 \\ 0 & 1\end{bmatrix} = \begin{bmatrix}a & b \\ c & d\end{bmatrix}
$$
in the $2 \times 2$ case.
In the $n \times n$ case:
- The first matrix should be a "stretched out" version of the $n\times n$ identity matrix $I_n$: repeat each column of $I_n$, $n$ times. In terms of the Kronecker product we can write this matrix as $I_n \otimes \mathbf j^{\mathsf T}$, where $\mathbf j \in \mathbb R^n$ is the all-$1$ vector.
- The second matrix should be $n$ copies of $I_n$ stacked on top of each other. In terms of the Kronecker product we can write this as $\mathbf j \otimes I_n$.
Without the diagonal operator, there is no single matrix multiplication that will produce the result. If we do any kind of multiplication with vector $\mathbf x$, the resulting matrix will always have column rank at most $1$.
However, as the other answer points out, we can add together $n$ different matrix multiplications, getting a solution of the form $\sum_{i=1}^n A_i \mathbf x B_i$. Written in terms of the Kronecker product, this solution is
$$
\sum_{i=1}^n (I_n \otimes (\mathbf e_i)^{\mathsf T})\mathbf x (\mathbf e_i)^{\mathsf T}
$$
where $\mathbf e_i$ is the $i^{\text{th}}$ standard basis vector: the $i^{\text{th}}$ column of $I_n$.
reshape. – Rodrigo de Azevedo Feb 06 '22 at 17:17