text: The paper is easy to read  for a physicist,  but I am not sure how useful it would be for ICLR...  it is not clear for me it there is an interest for quantum problems in this conference.  This is something I will let to the Area Chair to deceede.  Other than this, the paper is interesting, certainly correct, and provides a nice perspective on the future of learning with quantum computers.  I like the  quantum "boltzmann machine" problems.  I feel, however, but it might be a bit far from the main interest of the conference. Comments: * What the authors called "Free energy-based reinforcement learning" seems to me just the minimization / maximiation of the free energy.  This is simply maximum likelihood applied to the free energy  and I think that calling it "reinforcement learning" is not only wrong, but also is very confusing, given this is usually reserved to an entirely different learning process. * While i liked the introduction of the quantum Boltzmann machine, I would be happy to learn what they can do?  Are these useful, for instance, to study correlated fermions/bosons?  The paper does not explain why one should be concerns with these devices. * The fact that the simulation on a classical computer agrees with the one on a quantum computer is promising,  but I would say that this shows that, so far, there is not yet a clear advantage in using a quantum computer.  This might change, but in the mean time, what is the benefits for the ICLR community?