If the columns of a square matrix are orthonormal, then the matrix is said to be orthogonal.
Note: The following are equivalent for a $n \times n$ matrix $P$.
$1)P$ is invertible and $P^{-1}=P^{T}$
$2)$ The rows of $P$ are orthonormal
$3)$ The columns of $P$ are orthonormal.
Hint:
First recall that condition $(1)$ is equivalent to $PP^{T}=I$ because of the fact that a square matrix is invertible if and only if $A^{T}$ is invertible. Let $x_1,x_2,\ldots,x_n$ denote the rows of $P$. Then $x^T_j$ is the $j$th column of $P^{T}$, so the $(i,j)$-entry of $PP^{T}$ is $x_i \cdot x_j$. Thus $PP^{T}=I$ means that $x_i \cdot x_j=0$ if $i \neq j$ and $x_i \cdot x_j=1$ if $i=j$. Hence condition $(1)$ is equivalent to $(2)$. The proof of the equivalence of $(1)$ and $(3)$ is similar.
If the columns of $Q$ are an orthonormal set, then that is equivalent to "the matrix is orthogonal", and since $I = Q^TQ = QQ^T$ and $(Q^T)^T = Q$, it follows that if $Q$ is orthogonal then so is $Q^T$, hence the columns of $Q^T$ (i.e., the rows of $Q$) form an orthonormal set as well.