  Adding self-loops at each vertex of a graph improves the performance of
quantum walks algorithms over loopless algorithms. Many works approach quantum
walks to search for a single marked vertex. In this article, we experimentally
address several problems related to quantum walk in the hypercube with
self-loops to search for multiple marked vertices. We first investigate the
quantum walk in the loopless hypercube. We saw that neighbor vertices are also
amplified and that approximately $1/2$ of the system energy is concentrated in
them. We show that the optimal value of $l$ for a single marked vertex is not
optimal for multiple marked vertices. We define a new value of $l = (n/N)\cdot
k$ to search multiple marked vertices. Next, we use this new value of $l$ found
to analyze the search for multiple marked vertices non-adjacent and show that
the probability of success is close to $1$. We also use the new value of $l$
found to analyze the search for several marked vertices that are adjacent and
show that the probability of success is directly proportional to the density of
marked vertices in the neighborhood. We also show that, in the case where
neighbors are marked, if there is at least one non-adjacent marked vertex, the
probability of success increases to close to $1$. The results found show that
the self-loop value for the quantum walk in the hypercube to search for several
marked vertices is $l = (n / N) \cdot k $.
