  We present extensive simulations of the recently reported quantum version of
the well-known Hopfield Neural Network to explore its emergent behavior. The
system is constituted of a network of $N$ qubits oscillating at a given
$\Omega$ frequency and which are coupled via Lindblad jump operators that
depend on local fields $h_i$ depending on some given stored patterns. In
agreement with previous results, our simulations show pattern-antipattern
oscillations of the overlaps with the stored patterns similar to those reported
within a mean-field description of such a system, and which are due to
metastability originated by the quantum effect driven by the $s_x^i$ qubit
operators. In simulations, we observe that such oscillations are stochastic due
to the inherent metastability of the pattern attractors induced by the quantum
term and disappear in finite systems when one averages over many quantum
trajectories. In addition, we report the system behavior when the number of
stored patterns enlarges, for the minimum temperature we can reach in
simulations (namely $T=0.005$). Our study reveals that the quantum term of the
Hamiltonian has a negative effect on storage capacity, decreasing the overlap
with the starting memory pattern for increased values of $\Omega$ and the
number of stored patterns. However, although the initial pattern destabilizes
due to quantum oscillations, other patterns can be retrieved and remain stable
for a large number of stored patterns, implying a quantum-dependent nonlinear
relationship between the recall process and the number of stored patterns.
