  We consider the problems of testing and learning quantum $k$-junta channels,
which are $n$-qubit to $n$-qubit quantum channels acting non-trivially on at
most $k$ out of $n$ qubits and leaving the rest of qubits unchanged. We show
the following.
  1. An $O\left(k\right)$-query algorithm to distinguish whether the given
channel is $k$-junta channel or is far from any $k$-junta channels, and a lower
bound $\Omega\left(\sqrt{k}\right)$ on the number of queries;
  2. An $\widetilde{O}\left(4^k\right)$-query algorithm to learn a $k$-junta
channel, and a lower bound $\Omega\left(4^k/k\right)$ on the number of queries.
  This gives the first junta channel testing and learning results, and
partially answers an open problem raised by Chen et al. (2023). In order to
settle these problems, we develop a Fourier analysis framework over the space
of superoperators and prove several fundamental properties, which extends the
Fourier analysis over the space of operators introduced in Montanaro and
Osborne (2010).
  Besides, we introduce $\textit{Influence-Sample}$ to replace
$\textit{Fourier-Sample}$ proposed in Atici and Servedio (2007). Our
$\textit{Influence-Sample}$ includes only single-qubit operations and results
in only constant-factor decrease in efficiency.
