In earlier work, this author has explained the robustness of nonlinear multilayer machine learning algorithms in terms of an intrinsic chaos of the logistic map. Moreover we have connected that dynamics to a spectral concentration which occurs in bounded-to-free quantum transitions. From these, one may formulate a fundamental irreversibility common to both machine and quantum learning. Second, in recent work this author has treated both the Bell and Zeno paradoxes of quantum measurement theory. Deep unresolved issues are exposed and analyzed. A fundamental new theorem on quantum mechanical reversiblity is presented. From such viewpoint, one may see more deeply the issue of decoherence in any quantum computing architecture. Third, in our examinations of human learning, we compared actual human decision processes against those of several A.I. learning schemes. We were struck by the repeated tendency of humans to go to great lengths to avoid a choice that includes a contradiction. That will be contrasted with quantum learning, which permits, at least probabilistically, contradictory conclusions.