  Sampling random variables following a Boltzmann distribution is an NP-hard
problem involved in various applications such as training of \textit{Boltzmann
machines}, a specific kind of neural network. Several attempts have been made
to use a D-Wave quantum computer to sample such a distribution, as this could
lead to significant speedup in these applications. Yet, at present, several
challenges remain to efficiently perform such sampling. We detail the various
obstacles and explain the remaining difficulties in solving the sampling
problem on a D-wave machine.
